#=================================================== # Set the current working directory #===================================================
import os
os.chdir("/Users/eklavya/projects/education/formalEducation/DataScience/DataScienceAssignments/HealthCare/Capstone/")
#=================================================== # Libraries to be used #===================================================
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(tidyr)
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
library(corrplot)
## corrplot 0.84 loaded
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
library(ggthemes)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
#=================================================== # Utility functions to be used #===================================================
# Function to replace "Not Available" to NA's
replace_NA <- function(x, y, z) {
x[which(x == y)] <- z
return(x)
}
func_numeric <- function(x) {
x <- as.numeric(x)
return(x)
}
func_rename <- function(x) {
x %>% rename_at(vars(-Provider.ID), function(y) paste0(y, "_score"))
}
# Function to scale the values.
negative_zscore <- function(i) {
return((mean(i, na.rm = T) - i) / (sd(i, na.rm = T)))
}
positive_zscore <- function(i) {
return((i - mean(i, na.rm = T)) / (sd(i, na.rm = T)))
}
#### Function to treat the outliers
treat_outliers <- function(df) {
for (colmn in 2:(ncol(df))) {
qtl = quantile(df[, colmn], probs = seq(0, 1, 0.00001), na.rm = T)
df[, colmn][which(df[, colmn] <= qtl[0.00125 * length(qtl)])] <- qtl[0.00125 * length(qtl)]
df[, colmn][which(df[, colmn] >= qtl[0.99875 * length(qtl)])] <- qtl[0.99875 * length(qtl)]
}
return(df)
}
#=================================================== # 1. Readmission - Load “Readmissions and Deaths - Hospital.csv” file into read_raw #===================================================
read_rawdata <- read.csv("Readmissions and Deaths - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
dim(read_rawdata) ## 67452 rows and 18 columns
## [1] 67452 18
#[1] 67452 18
str(read_rawdata)
## 'data.frame': 67452 obs. of 18 variables:
## $ Provider.ID : int 10001 10001 10001 10001 10001 10001 10001 10001 10001 10001 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "SOUTHEAST ALABAMA MEDICAL CENTER" "SOUTHEAST ALABAMA MEDICAL CENTER" "SOUTHEAST ALABAMA MEDICAL CENTER" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "1108 ROSS CLARK CIRCLE" "1108 ROSS CLARK CIRCLE" "1108 ROSS CLARK CIRCLE" ...
## $ City : chr "DOTHAN" "DOTHAN" "DOTHAN" "DOTHAN" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 36301 36301 36301 36301 36301 36301 36301 36301 36301 ...
## $ County.Name : chr "HOUSTON" "HOUSTON" "HOUSTON" "HOUSTON" ...
## $ Phone.Number : num 3.35e+09 3.35e+09 3.35e+09 3.35e+09 3.35e+09 ...
## $ Measure.Name : chr "Acute Myocardial Infarction (AMI) 30-Day Mortality Rate" "Death rate for CABG" "Death rate for chronic obstructive pulmonary disease (COPD) patients" "Heart failure (HF) 30-Day Mortality Rate" ...
## $ Measure.ID : chr "MORT_30_AMI" "MORT_30_CABG" "MORT_30_COPD" "MORT_30_HF" ...
## $ Compared.to.National: chr "No Different than the National Rate" "No Different than the National Rate" "No Different than the National Rate" "No Different than the National Rate" ...
## $ Denominator : int 733 278 586 797 599 512 781 273 707 981 ...
## $ Score : num 12.5 4.2 9.3 12.4 15.5 15.4 16.5 15.1 21.1 21.4 ...
## $ Lower.Estimate : num 10.6 2.6 7.3 10.4 13 12.8 14.6 12.3 18.7 19.2 ...
## $ Higher.Estimate : num 14.9 6.8 11.8 14.6 18.5 18.6 18.8 18.5 23.8 23.7 ...
## $ Footnote : chr "" "" "" "" ...
## $ Measure.Start.Date : chr "07/01/2012" "07/01/2012" "07/01/2012" "07/01/2012" ...
## $ Measure.End.Date : chr "06/30/2015" "06/30/2015" "06/30/2015" "06/30/2015" ...
unique(read_rawdata$Score)
## [1] 12.5 4.2 9.3 12.4 15.5 15.4 16.5 15.1 21.1 21.4 5.1 18.7 12.7 16.0
## [15] NA 7.6 20.8 16.7 18.0 21.9 5.7 14.9 16.4 13.4 4.1 7.1 15.6 18.2
## [29] 17.9 16.1 15.2 19.8 20.6 5.0 12.0 14.4 18.8 16.6 19.9 17.3 8.2 15.7
## [43] 19.2 23.1 13.9 3.7 7.4 13.8 12.6 17.7 15.0 19.6 15.3 11.3 16.8 9.2
## [57] 26.8 15.8 23.4 19.4 12.8 14.7 3.6 8.1 14.0 21.5 17.1 8.7 12.9 19.1
## [71] 23.0 22.4 15.9 18.3 13.5 6.4 12.2 14.3 21.3 14.8 7.7 4.5 9.1 18.1
## [85] 19.5 23.3 4.7 18.6 4.8 9.5 10.6 17.2 19.0 4.9 16.9 13.0 4.4 8.0
## [99] 12.1 23.2 5.9 10.5 21.6 16.3 2.3 18.9 23.7 7.0 9.4 20.1 21.7 8.3
## [113] 20.2 16.2 22.8 4.6 7.2 11.5 13.2 3.9 14.5 20.3 24.0 13.1 1.8 11.7
## [127] 13.7 12.3 7.5 19.7 21.0 6.7 6.6 11.4 10.7 17.4 22.2 11.2 3.1 7.8
## [141] 14.2 22.3 17.5 14.6 20.0 11.6 13.6 22.5 4.3 10.4 20.4 11.9 17.0 10.9
## [155] 13.3 17.8 5.3 14.1 18.5 22.6 8.6 20.7 20.9 3.0 6.8 6.2 22.9 7.9
## [169] 5.5 21.8 6.9 20.5 9.0 22.0 3.5 8.5 5.2 9.9 22.1 18.4 10.3 19.3
## [183] 10.0 23.6 17.6 21.2 10.1 2.7 22.7 5.4 9.7 9.6 4.0 2.8 11.8 23.8
## [197] 8.9 7.3 10.8 9.8 2.9 8.4 24.6 3.8 5.6 11.0 6.0 24.8 3.2 2.1
## [211] 24.4 11.1 23.9 2.4 3.4 23.5 24.7 26.2 8.8 24.3 25.2 26.1 24.2 24.5
## [225] 24.9 3.3 2.6 6.5 1.9 2.2 25.3 6.1 10.2 25.0 24.1 26.0 5.8 6.3
## [239] 2.5 26.3 25.9 2.0 1.4 25.5 26.4 27.0 25.4 25.7 26.7 25.1 25.8 31.3
## [253] 1.7 1.5 26.9 25.6 27.2 27.4 1.6
# We will filter only those columns which are needed as per the mentor.
read_meas_list = c("READM_30_AMI", "READM_30_CABG", "READM_30_COPD", "READM_30_HF", "READM_30_HIP_KNEE", "READM_30_HOSP_WIDE", "READM_30_PN", "READM_30_STK")
read_hosp <- read_rawdata[, c(1:8)]
read_hosp <- read_hosp[!duplicated(read_hosp),]
read_meas <- read_rawdata[, c(1, 10, 13)]
read_meas$Score <- func_numeric(read_meas$Score)
read_meas <- subset(read_meas, Measure.ID %in% read_meas_list)
str(read_meas)
## 'data.frame': 38544 obs. of 3 variables:
## $ Provider.ID: int 10001 10001 10001 10001 10001 10001 10001 10001 10005 10005 ...
## $ Measure.ID : chr "READM_30_AMI" "READM_30_CABG" "READM_30_COPD" "READM_30_HF" ...
## $ Score : num 16.5 15.1 21.1 21.4 5.1 15.4 18.7 12.7 16.7 NA ...
read_meas_score <- spread(read_meas, Measure.ID, Score)
str(read_meas_score)
## 'data.frame': 4818 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ READM_30_AMI : num 16.5 16.7 16.1 NA NA 17.7 16.1 17.7 NA NA ...
## $ READM_30_CABG : num 15.1 NA 15.2 NA NA 15 NA 15.8 NA NA ...
## $ READM_30_COPD : num 21.1 18 19.8 19.9 19.2 19.6 19.2 17.9 NA 23 ...
## $ READM_30_HF : num 21.4 21.9 20.6 21.1 23.1 19.8 23.4 21.5 NA 22.4 ...
## $ READM_30_HIP_KNEE : num 5.1 5.7 5 NA NA 5.1 NA 5 NA 4.1 ...
## $ READM_30_HOSP_WIDE: num 15.4 14.9 15.4 16.6 15.7 15.3 15.3 14.7 NA 15.9 ...
## $ READM_30_PN : num 18.7 16.4 17.9 17.3 16 16.7 19.4 17.1 NA 18.3 ...
## $ READM_30_STK : num 12.7 13.4 12 12.7 NA 11.3 12.8 12.4 NA 13.5 ...
read_meas_score <- func_rename(read_meas_score)
str(read_meas_score)
## 'data.frame': 4818 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ READM_30_AMI_score : num 16.5 16.7 16.1 NA NA 17.7 16.1 17.7 NA NA ...
## $ READM_30_CABG_score : num 15.1 NA 15.2 NA NA 15 NA 15.8 NA NA ...
## $ READM_30_COPD_score : num 21.1 18 19.8 19.9 19.2 19.6 19.2 17.9 NA 23 ...
## $ READM_30_HF_score : num 21.4 21.9 20.6 21.1 23.1 19.8 23.4 21.5 NA 22.4 ...
## $ READM_30_HIP_KNEE_score : num 5.1 5.7 5 NA NA 5.1 NA 5 NA 4.1 ...
## $ READM_30_HOSP_WIDE_score: num 15.4 14.9 15.4 16.6 15.7 15.3 15.3 14.7 NA 15.9 ...
## $ READM_30_PN_score : num 18.7 16.4 17.9 17.3 16 16.7 19.4 17.1 NA 18.3 ...
## $ READM_30_STK_score : num 12.7 13.4 12 12.7 NA 11.3 12.8 12.4 NA 13.5 ...
readmission <- read_meas_score
# # We will use negative zscore scaling as high readmissions implies the Hospital is not doing well in terms of patient treatment quality.
readmission[, 2:ncol(readmission)] <- sapply(readmission[, -1], negative_zscore)
str(readmission)
## 'data.frame': 4818 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ READM_30_AMI_score : num 0.409 0.2 0.826 NA NA ...
## $ READM_30_CABG_score : num -0.615 NA -0.704 NA NA ...
## $ READM_30_COPD_score : num -0.8668 1.5743 0.1569 0.0782 0.6294 ...
## $ READM_30_HF_score : num 0.3704 0.0364 0.9047 0.5707 -0.765 ...
## $ READM_30_HIP_KNEE_score : num -0.884 -1.968 -0.703 NA NA ...
## $ READM_30_HOSP_WIDE_score: num 0.215 0.821 0.215 -1.237 -0.148 ...
## $ READM_30_PN_score : num -1.105 0.496 -0.549 -0.131 0.774 ...
## $ READM_30_STK_score : num -0.125 -0.777 0.528 -0.125 NA ...
# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
readmission <- treat_outliers(readmission)
read_master <- merge(read_hosp, readmission, by = "Provider.ID")
dim(read_master)
## [1] 4818 16
# [1] 4818 16
str(read_master)
## 'data.frame': 4818 obs. of 16 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ READM_30_AMI_score : num 0.409 0.2 0.826 NA NA ...
## $ READM_30_CABG_score : num -0.615 NA -0.704 NA NA ...
## $ READM_30_COPD_score : num -0.8668 1.5743 0.1569 0.0782 0.6294 ...
## $ READM_30_HF_score : num 0.3704 0.0364 0.9047 0.5707 -0.765 ...
## $ READM_30_HIP_KNEE_score : num -0.884 -1.968 -0.703 NA NA ...
## $ READM_30_HOSP_WIDE_score: num 0.215 0.821 0.215 -1.237 -0.148 ...
## $ READM_30_PN_score : num -1.105 0.496 -0.549 -0.131 0.774 ...
## $ READM_30_STK_score : num -0.125 -0.777 0.528 -0.125 NA ...
write.csv(readmission, "cleaned_readmission_data.csv")
#=================================================== ## 2. Mortality - Load 2 Files “Readmissions and Deaths - Hospital.csv + Complications - Hospital.csv” into morality dataframe #===================================================
mort_rawdata1 <- read_rawdata
mort_rawdata2 <- read.csv("Complications - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
identical(names(mort_rawdata1), names(mort_rawdata2))
## [1] TRUE
# [1] TRUE
mort_rawdata <- rbind(mort_rawdata1, mort_rawdata2)
mort_meas_list = c("MORT_30_AMI", "MORT_30_CABG", "MORT_30_COPD", "MORT_30_HF", "MORT_30_PN", "MORT_30_STK", "PSI_4_SURG_COMP")
mort_hosp <- mort_rawdata[, c(1:8)]
mort_hosp = mort_hosp[!duplicated(mort_hosp),]
mort_meas <- mort_rawdata[, c(1, 10, 13)]
mort_meas <- subset(mort_meas, Measure.ID %in% mort_meas_list)
mort_meas$Score <- func_numeric(mort_meas$Score)
mort_meas_score <- spread(mort_meas, Measure.ID, Score)
mort_meas_score <- func_rename(mort_meas_score)
str(mort_meas_score)
## 'data.frame': 4818 obs. of 8 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ MORT_30_AMI_score : num 12.5 16 16.7 NA NA 13.9 16.8 14.7 NA 15.6 ...
## $ MORT_30_CABG_score : num 4.2 NA 4.1 NA NA 3.7 NA 3.6 NA NA ...
## $ MORT_30_COPD_score : num 9.3 7.6 7.1 9.3 8.2 7.4 9.2 8.1 NA 8.7 ...
## $ MORT_30_HF_score : num 12.4 15.5 15.6 14.4 12.7 13.8 12.5 14 NA 12.9 ...
## $ MORT_30_PN_score : num 15.5 20.8 18.2 18.8 15.7 17.9 26.8 16.1 NA 19.1 ...
## $ MORT_30_STK_score : num 15.4 15.5 17.9 16.6 NA 12.6 15.8 15.8 NA 15.4 ...
## $ PSI_4_SURG_COMP_score: num 168 179 198 NA NA ...
mortality <- mort_meas_score
# Mortality indicates the death rate, higher the number worser is the hospital or provider.
# Since it is related to death rate we will use negative z-score formula.
mortality[, 2:ncol(mortality)] <- sapply(mortality[, -1], negative_zscore)
str(mortality)
## 'data.frame': 4818 obs. of 8 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.1 NA NA ...
## $ MORT_30_CABG_score : num -0.996 NA -0.881 NA NA ...
## $ MORT_30_COPD_score : num -1.094 0.434 0.883 -1.094 -0.105 ...
## $ MORT_30_HF_score : num -0.166 -2.286 -2.355 -1.534 -0.371 ...
## $ MORT_30_PN_score : num 0.428 -2.103 -0.862 -1.148 0.332 ...
## $ MORT_30_STK_score : num -0.282 -0.342 -1.784 -1.003 NA ...
## $ PSI_4_SURG_COMP_score: num -1.71 -2.3 -3.35 NA NA ...
# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
mortality <- treat_outliers(mortality)
mort_master <- merge(mort_hosp, mortality, by = "Provider.ID")
dim(mort_master)
## [1] 4818 15
#[1] 4818 15
str(mort_master)
## 'data.frame': 4818 obs. of 15 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.1 NA NA ...
## $ MORT_30_CABG_score : num -0.996 NA -0.881 NA NA ...
## $ MORT_30_COPD_score : num -1.094 0.434 0.883 -1.094 -0.105 ...
## $ MORT_30_HF_score : num -0.166 -2.286 -2.355 -1.534 -0.371 ...
## $ MORT_30_PN_score : num 0.428 -2.103 -0.862 -1.148 0.332 ...
## $ MORT_30_STK_score : num -0.282 -0.342 -1.784 -1.003 NA ...
## $ PSI_4_SURG_COMP_score: num -1.71 -2.3 -3.33 NA NA ...
write.csv(mortality, "cleaned_mortality_data.csv")
safe_rawdata1 <- mort_rawdata
safe_rawdata2 <- read.csv("Healthcare Associated Infections - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
safe_rawdata1 = safe_rawdata1[, c(1:8, 10, 13)]
safe_rawdata2 = safe_rawdata2[, c(1:8, 10, 12)]
identical(names(safe_rawdata1), names(safe_rawdata2))
## [1] TRUE
# [1] TRUE
safe_rawdata <- rbind(safe_rawdata1, safe_rawdata2)
safe_meas_list = c("HAI_1_SIR", "HAI_2_SIR", "HAI_3_SIR", "HAI_4_SIR", "HAI_5_SIR", "HAI_6_SIR", "COMP_HIP_KNEE", "PSI_90_SAFETY")
safe_hosp <- safe_rawdata[, c(1:8)]
safe_hosp = safe_hosp[!duplicated(safe_hosp),]
safe_meas <- safe_rawdata[, c(1, 9:10)]
safe_meas <- subset(safe_meas, Measure.ID %in% safe_meas_list)
safe_meas$Score <- func_numeric(safe_meas$Score)
safe_meas_score <- spread(safe_meas, Measure.ID, Score)
safe_meas_score <- func_rename(safe_meas_score)
safety <- safe_meas_score
# The HAI measures are related to infections contracted by the patients during their stay in the hospital
# we will negative zscore here as well
safety[, 2:ncol(safety)] <- sapply(safety[, -1], negative_zscore)
str(safety)
## 'data.frame': 4818 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ COMP_HIP_KNEE_score: num -1.355 0.0745 -1.355 NA NA ...
## $ HAI_1_SIR_score : num -2.351 -1.023 0.389 NA NA ...
## $ HAI_2_SIR_score : num -2.088 0.05 -0.357 1.054 NA ...
## $ HAI_3_SIR_score : num -1.135 0.723 0.818 NA NA ...
## $ HAI_4_SIR_score : num 1.02 NA NA NA NA ...
## $ HAI_5_SIR_score : num 0.647 -0.457 -0.312 NA NA ...
## $ HAI_6_SIR_score : num 0.0566 0.7984 0.5887 1.5849 0.4489 ...
## $ PSI_90_SAFETY_score: num 1.2108 0.2304 -0.1156 0.5764 -0.0579 ...
# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
safety <- treat_outliers(safety)
safe_master <- merge(safe_hosp, safety, "Provider.ID")
dim(safe_master)
## [1] 4818 16
# [1] 4818 16
write.csv(safety, "cleaned_safety_data.csv")
expe_rawdata <- read.csv("HCAHPS - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
expe_meas_list = c("H_CLEAN_LINEAR_SCORE", "H_COMP_1_LINEAR_SCORE", "H_COMP_2_LINEAR_SCORE", "H_COMP_3_LINEAR_SCORE", "H_COMP_4_LINEAR_SCORE", "H_COMP_5_LINEAR_SCORE", "H_COMP_6_LINEAR_SCORE", "H_COMP_7_LINEAR_SCORE", "H_HSP_RATING_LINEAR_SCORE", "H_QUIET_LINEAR_SCORE", "H_RECMND_LINEAR_SCORE")
names(expe_rawdata)[names(expe_rawdata) == "HCAHPS.Question"] <- "Measure.Name"
names(expe_rawdata)[names(expe_rawdata) == "HCAHPS.Measure.ID"] <- "Measure.ID"
names(expe_rawdata)[names(expe_rawdata) == "HCAHPS.Linear.Mean.Value"] <- "Score"
expe_hosp <- expe_rawdata[, c(1:8)]
expe_hosp = expe_hosp[!duplicated(expe_hosp),]
expe_meas <- expe_rawdata[, c(1, 9, 16)]
expe_meas <- subset(expe_meas, Measure.ID %in% expe_meas_list)
expe_meas$Score <- func_numeric(expe_meas$Score)
expe_meas_score <- spread(expe_meas, Measure.ID, Score)
experience <- expe_meas_score
# It measures cleanliness, patient hospitality and doctors/staff communication,
# hospital environment etc. We will use positive zscore here
experience[, 2:ncol(experience)] <- sapply(experience[, -1], positive_zscore)
str(experience)
## 'data.frame': 4818 obs. of 12 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ H_CLEAN_LINEAR_SCORE : num -0.853 -1.112 -1.112 0.442 NA ...
## $ H_COMP_1_LINEAR_SCORE : num -0.524 -0.129 -0.129 -0.129 NA ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0412 0.8596 0.8596 1.678 NA ...
## $ H_COMP_3_LINEAR_SCORE : num -1.2 -0.29 -0.517 0.392 NA ...
## $ H_COMP_4_LINEAR_SCORE : num -0.606 0.167 -0.22 0.553 NA ...
## $ H_COMP_5_LINEAR_SCORE : num -0.415 0.285 -0.182 0.751 NA ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0276 0.3087 -1.0964 -0.2534 NA ...
## $ H_COMP_7_LINEAR_SCORE : num 0.165 -0.183 -0.532 0.165 NA ...
## $ H_HSP_RATING_LINEAR_SCORE: num 0.0842 0.3915 -1.1451 -0.5304 NA ...
## $ H_QUIET_LINEAR_SCORE : num 0.969 0.577 0.577 1.751 NA ...
## $ H_RECMND_LINEAR_SCORE : num 0.45 0.221 -0.923 -0.465 NA ...
# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
experience <- treat_outliers(experience)
expe_master <- merge(expe_hosp, experience, by = "Provider.ID")
dim(expe_master)
## [1] 4818 19
# [1] 4818 19
write.csv(experience, "cleaned_experience_data.csv")
medi_rawdata <- read.csv("Outpatient Imaging Efficiency - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
medi_meas_list = c("OP_10", "OP_11", "OP_13", "OP_14", "OP_8")
medi_hosp <- medi_rawdata[, c(1:8)]
medi_hosp = medi_hosp[!duplicated(medi_hosp),]
medi_meas <- medi_rawdata[, c(1, 9, 11)]
medi_meas <- subset(medi_meas, Measure.ID %in% medi_meas_list)
medi_meas$Score <- as.numeric(medi_meas$Score)
medi_meas_score <- spread(medi_meas, Measure.ID, Score)
medi_meas_score <- func_rename(medi_meas_score)
medical <- medi_meas_score
# Unecessary usage of imaging tests, lower the better. We will use the negative zscore
medical[, 2:ncol(medical)] <- sapply(medical[, -1], negative_zscore)
str(medical)
## 'data.frame': 4818 obs. of 6 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ OP_10_score: num 0.251 -0.423 -0.277 -1.498 0.524 ...
## $ OP_11_score: num 0.389 -1.204 -0.245 -0.502 NA ...
## $ OP_13_score: num -1.2 -0.305 2.33 NA NA ...
## $ OP_14_score: num 0.207 -0.649 -0.97 NA 1.171 ...
## $ OP_8_score : num 0.3 -0.378 -0.782 NA NA ...
# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
medical <- treat_outliers(medical)
medi_master <- merge(medi_hosp, medical, by = "Provider.ID")
dim(medi_master)
## [1] 4818 13
# [1] 4818 13
write.csv(medical, "cleaned_medical_data.csv")
time_rawdata <- read.csv("Timely and Effective Care - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
time_meas_list = c("ED_1b", "ED_2b", "OP_18b", "OP_20", "OP_21", "OP_3b", "OP_5")
time_hosp <- time_rawdata[, c(1:8)]
time_hosp = time_hosp[!duplicated(time_hosp),]
time_meas <- time_rawdata[, c(1, 10, 12)]
time_meas <- subset(time_meas, Measure.ID %in% time_meas_list)
time_meas$Score <- as.numeric(time_meas$Score)
time_meas_score <- spread(time_meas, Measure.ID, Score)
time_meas_score <- func_rename(time_meas_score)
timeliness <- time_meas_score
# All the measures in timeliness indicate the average time the patient had to wait
# before being attended by the doctors or concerned specialists. We will use negative zscore
timeliness[, 2:ncol(timeliness)] <- sapply(timeliness[, -1], negative_zscore)
str(timeliness)
## 'data.frame': 4818 obs. of 8 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ ED_1b_score : num 0.0892 0.3415 0.5938 0.5743 0.9527 ...
## $ ED_2b_score : num 0.508 0.463 0.357 0.508 0.69 ...
## $ OP_18b_score: num -1.266 0.616 0.235 0.568 1.068 ...
## $ OP_20_score : num -2.414 -0.049 1.009 -0.733 -0.049 ...
## $ OP_21_score : num -2.587 -0.38 -0.267 -2.078 0.243 ...
## $ OP_3b_score : num NA NA NA NA NA ...
## $ OP_5_score : num NA -0.714 NA 0.248 NA ...
# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
timeliness <- treat_outliers(timeliness)
time_master <- merge(time_hosp, timeliness, by = "Provider.ID")
dim(time_master)
## [1] 4818 15
# [1] 4818 15
write.csv(timeliness, "cleaned_timeliness_data.csv")
effe_rawdata <- time_rawdata
effe_meas_list = c("CAC_3", "IMM_2", "IMM_3_OP_27_FAC_ADHPCT", "OP_22", "OP_23", "OP_29", "OP_30", "OP_4", "PC_01", "STK_4", "STK_5", "STK_6", "STK_8", "VTE_1", "VTE_2", "VTE_3", "VTE_5", "VTE_6")
effe_hosp <- effe_rawdata[, c(1:8)]
effe_hosp = effe_hosp[!duplicated(effe_hosp),]
effe_meas <- effe_rawdata[, c(1, 10, 12)]
effe_meas <- subset(effe_meas, Measure.ID %in% effe_meas_list)
effe_meas$Score <- as.numeric(effe_meas$Score)
effe_meas_score <- spread(effe_meas, Measure.ID, Score)
effe_meas_score <- func_rename(effe_meas_score)
effectiveness <- effe_meas_score
# Effectiveness has some columns for which the value higher is better, and few the score lower is better
# We will both postive and negative zscores here for the filtered columns
positive_measures <- c(2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18)
negative_measures <- c(5, 10, 19)
effectiveness[, positive_measures] <- sapply(effectiveness[, positive_measures], positive_zscore)
effectiveness[, negative_measures] <- sapply(effectiveness[, negative_measures], negative_zscore)
str(effectiveness)
## 'data.frame': 4818 obs. of 19 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ CAC_3_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ IMM_2_score : num 0.364 0.53 0.613 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.233 -0.099 0.169 -2.11 -2.781 ...
## $ OP_22_score : num -1.201 -0.108 0.438 0.438 0.438 ...
## $ OP_23_score : num NA 0.783 NA NA NA ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.546 NA -1.232 NA ...
## $ PC_01_score : num 0.539 0.324 0.539 NA NA ...
## $ STK_4_score : num -1.23 NA NA NA NA ...
## $ STK_5_score : num -0.0249 0.1932 0.1932 0.4114 NA ...
## $ STK_6_score : num 0.446 -0.915 0.198 -3.019 NA ...
## $ STK_8_score : num -0.57 0.618 -0.296 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.381 -0.45 -1.282 0.381 NA ...
## $ VTE_3_score : num -0.278 0.819 -0.887 NA NA ...
## $ VTE_5_score : num -0.2536 0.6352 -0.0759 NA NA ...
## $ VTE_6_score : num 0.416 NA 0.416 NA NA ...
# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
effectiveness <- treat_outliers(effectiveness)
effe_master <- merge(effe_hosp, effectiveness, by = "Provider.ID")
dim(effe_master)
## [1] 4818 26
# [1] 4818 26
write.csv(effectiveness, "cleaned_effectiveness_data.csv")
merge1 <- merge(read_master, mort_master)
merge2 <- merge(merge1, safe_master)
merge3 <- merge(merge2, expe_master)
merge4 <- merge(merge3, medi_master)
merge5 <- merge(merge4, time_master)
merge6 <- merge(merge5, effe_master)
str(merge6)
## 'data.frame': 4818 obs. of 72 variables:
## $ Provider.ID : int 100001 100002 100006 100007 100008 100009 10001 100012 100014 100017 ...
## $ Hospital.Name : chr "UF HEALTH JACKSONVILLE" "BETHESDA HOSPITAL EAST" "ORLANDO HEALTH" "FLORIDA HOSPITAL" ...
## $ Address : chr "655 W 8TH ST" "2815 S SEACREST BLVD" "52 W UNDERWOOD ST" "601 E ROLLINS ST" ...
## $ City : chr "JACKSONVILLE" "BOYNTON BEACH" "ORLANDO" "ORLANDO" ...
## $ State : chr "FL" "FL" "FL" "FL" ...
## $ ZIP.Code : int 32209 33435 32806 32803 33176 33136 36301 33901 32170 32114 ...
## $ County.Name : chr "DUVAL" "PALM BEACH" "ORANGE" "ORANGE" ...
## $ Phone.Number : num 9.04e+09 5.62e+09 3.22e+09 4.07e+09 7.87e+09 ...
## $ READM_30_AMI_score : num -2.618 0.722 0.304 -0.948 1.348 ...
## $ READM_30_CABG_score : num -1.946 0.627 0.805 -2.39 0.716 ...
## $ READM_30_COPD_score : num -0.1581 -0.5518 0.0782 -2.9142 0.3144 ...
## $ READM_30_HF_score : num -1.967 -0.364 1.305 -2.034 -0.431 ...
## $ READM_30_HIP_KNEE_score : num -0.703 1.284 -0.342 -1.426 1.103 ...
## $ READM_30_HOSP_WIDE_score : num -1.963 0.457 -0.511 -3.658 0.821 ...
## $ READM_30_PN_score : num -1.245 0.426 0.287 -1.593 -0.479 ...
## $ READM_30_STK_score : num -1.15 1.367 -0.777 -2.175 -0.87 ...
## $ MORT_30_AMI_score : num -2.504 1.489 -0.907 -0.667 0.451 ...
## $ MORT_30_CABG_score : num -0.308 -0.193 0.495 -2.257 1.068 ...
## $ MORT_30_COPD_score : num -0.0154 -0.2851 0.8833 1.7821 1.6024 ...
## $ MORT_30_HF_score : num 1.2026 0.1765 -0.0971 0.929 0.7922 ...
## $ MORT_30_PN_score : num 1.335 0.762 0.332 0.475 1.717 ...
## $ MORT_30_STK_score : num -1.9646 0.4992 0.0185 1.7612 1.3405 ...
## $ PSI_4_SURG_COMP_score : num -1.068 1.544 -0.49 0.998 0.144 ...
## $ COMP_HIP_KNEE_score : num -0.104 1.325 0.432 0.789 1.504 ...
## $ HAI_1_SIR_score : num -3.889 0.133 0.274 0.421 -0.492 ...
## $ HAI_2_SIR_score : num -0.4762 0.0302 -0.5797 0.1117 0.0236 ...
## $ HAI_3_SIR_score : num 0.087 -0.379 0.658 -0.216 0.237 ...
## $ HAI_4_SIR_score : num -2.088 0.228 0.309 0.488 -2.129 ...
## $ HAI_5_SIR_score : num -3.193 -1.519 0.639 0.287 0.15 ...
## $ HAI_6_SIR_score : num -1.204 0.395 -0.394 0.156 0.179 ...
## $ PSI_90_SAFETY_score : num -1.557 0.346 0.288 -0.635 -1.384 ...
## $ H_CLEAN_LINEAR_SCORE : num -1.888 -1.112 0.183 0.701 0.183 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.918 -0.918 -0.129 0.265 0.265 ...
## $ H_COMP_2_LINEAR_SCORE : num -0.777 -1.596 -0.368 -0.777 0.45 ...
## $ H_COMP_3_LINEAR_SCORE : num -0.9724 -1.4273 -0.29 -0.0626 -0.29 ...
## $ H_COMP_4_LINEAR_SCORE : num -1.379 -0.22 0.167 0.167 0.167 ...
## $ H_COMP_5_LINEAR_SCORE : num -0.4152 -1.3482 -0.182 0.5178 0.0513 ...
## $ H_COMP_6_LINEAR_SCORE : num -0.534 -1.939 -0.815 0.309 -0.253 ...
## $ H_COMP_7_LINEAR_SCORE : num -0.532 -0.532 0.165 0.513 0.861 ...
## $ H_HSP_RATING_LINEAR_SCORE : num -0.8378 -0.2231 0.0842 0.3915 0.3915 ...
## $ H_QUIET_LINEAR_SCORE : num -0.59675 -0.00976 0.38156 0.57722 0.38156 ...
## $ H_RECMND_LINEAR_SCORE : num -1.151 -0.236 0.221 0.679 0.908 ...
## $ OP_10_score : num -2.602 0.73 0.554 0.495 0.73 ...
## $ OP_11_score : num -2.197 0.423 0.474 0.474 0.44 ...
## $ OP_13_score : num 1.1862 NA 0.0925 -0.5537 -1.7468 ...
## $ OP_14_score : num 0.0469 -1.1843 -0.756 -0.3813 0.3681 ...
## $ OP_8_score : num 1.295 NA NA -0.349 NA ...
## $ ED_1b_score : num -1.735 0.215 -0.503 -0.629 -1.851 ...
## $ ED_2b_score : num -1.312 -0.478 -0.235 -0.281 -2.086 ...
## $ OP_18b_score : num -1.766 -0.123 -0.551 -1.075 -1.932 ...
## $ OP_20_score : num -0.609 0.76 -0.547 -0.36 -1.854 ...
## $ OP_21_score : num -0.946 -0.153 -0.55 -1.512 0.696 ...
## $ OP_3b_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ OP_5_score : num -5.14 NA NA NA NA ...
## $ CAC_3_score : num NA NA 0.606 NA NA ...
## $ IMM_2_score : num 0.281 0.53 0.197 0.53 0.613 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num 0.8395 -1.5068 0.0351 -0.3672 0.1691 ...
## $ OP_22_score : num -3.387 0.438 -0.108 -0.108 -0.108 ...
## $ OP_23_score : num -0.777 NA -0.777 -2.136 NA ...
## $ OP_29_score : num 0.981 -1.184 0.764 -1.364 -1.148 ...
## $ OP_30_score : num 0.844 0.2 0.629 -0.315 0.371 ...
## $ OP_4_score : num -2.48 NA NA NA NA ...
## $ PC_01_score : num 0.539 0.539 -0.75 0.539 0.539 ...
## $ STK_4_score : num 0.153 -0.179 0.374 0.374 0.594 ...
## $ STK_5_score : num -0.243 0.1932 -0.0249 -0.0249 0.4114 ...
## $ STK_6_score : num 0.446 -0.297 0.57 0.322 0.446 ...
## $ STK_8_score : num -0.113 0.618 0.253 0.435 0.618 ...
## $ VTE_1_score : num 0.334 0.489 0.412 0.412 0.567 ...
## $ VTE_2_score : num -0.118 -0.45 0.547 0.381 0.547 ...
## $ VTE_3_score : num -0.643 0.332 0.21 -1.253 0.819 ...
## $ VTE_5_score : num 0.635 0.369 0.191 0.102 0.635 ...
## $ VTE_6_score : num 0.416 0.416 0.212 0.212 0.416 ...
dim(merge6)
## [1] 4818 72
# [1] 4818 72
master_data_x <- merge6
Raw data is ready with all required 64 measures and 8 general columns
## Remove duplicate Data
master_data_x = master_data_x[!duplicated(master_data_x),]
dim(master_data_x) ## No duplicates found
## [1] 4818 72
# [1] 4818 72
## Remove NA Values
sapply(master_data_x, function(x) sum(length(which(is.na(x)))))
## Provider.ID Hospital.Name
## 0 0
## Address City
## 0 0
## State ZIP.Code
## 0 0
## County.Name Phone.Number
## 0 0
## READM_30_AMI_score READM_30_CABG_score
## 2655 3791
## READM_30_COPD_score READM_30_HF_score
## 1170 1168
## READM_30_HIP_KNEE_score READM_30_HOSP_WIDE_score
## 2087 423
## READM_30_PN_score READM_30_STK_score
## 729 2210
## MORT_30_AMI_score MORT_30_CABG_score
## 2430 3780
## MORT_30_COPD_score MORT_30_HF_score
## 1227 1200
## MORT_30_PN_score MORT_30_STK_score
## 730 2142
## PSI_4_SURG_COMP_score COMP_HIP_KNEE_score
## 3000 2104
## HAI_1_SIR_score HAI_2_SIR_score
## 2443 1929
## HAI_3_SIR_score HAI_4_SIR_score
## 2775 3962
## HAI_5_SIR_score HAI_6_SIR_score
## 2988 1546
## PSI_90_SAFETY_score H_CLEAN_LINEAR_SCORE
## 1594 1310
## H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE
## 1310 1310
## H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 1310 1310
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
## 1310 1310
## H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE
## 1310 1310
## H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 1310 1310
## OP_10_score OP_11_score
## 1189 1469
## OP_13_score OP_14_score
## 2585 2514
## OP_8_score ED_1b_score
## 3294 1222
## ED_2b_score OP_18b_score
## 1238 1234
## OP_20_score OP_21_score
## 1223 1505
## OP_3b_score OP_5_score
## 4425 2574
## CAC_3_score IMM_2_score
## 4643 1034
## IMM_3_OP_27_FAC_ADHPCT_score OP_22_score
## 711 1543
## OP_23_score OP_29_score
## 3608 2087
## OP_30_score OP_4_score
## 2191 2599
## PC_01_score STK_4_score
## 2296 3919
## STK_5_score STK_6_score
## 3276 2239
## STK_8_score VTE_1_score
## 2454 1200
## VTE_2_score VTE_3_score
## 1883 2332
## VTE_5_score VTE_6_score
## 2587 3560
# Provider.ID Hospital.Name Address City State
# 0 0 0 0 0
# ZIP.Code County.Name Phone.Number READM_30_AMI_score READM_30_CABG_score
# 0 0 0 2655 3791
# READM_30_COPD_score READM_30_HF_score READM_30_HIP_KNEE_score READM_30_HOSP_WIDE_score READM_30_PN_score
# 1170 1168 2087 423 729
# READM_30_STK_score MORT_30_AMI_score MORT_30_CABG_score MORT_30_COPD_score MORT_30_HF_score
# 2210 2430 3780 1227 1200
# MORT_30_PN_score MORT_30_STK_score PSI_4_SURG_COMP_score COMP_HIP_KNEE_score HAI_1_SIR_score
# 730 2142 3000 2104 2443
# HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
# 1929 2775 3962 2988 1546
# PSI_90_SAFETY_score H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE
# 1594 1310 1310 1310 1310
# H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE
# 1310 1310 1310 1310 1310
# H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE OP_10_score OP_11_score OP_13_score
# 1310 1310 1189 1469 2585
# OP_14_score OP_8_score ED_1b_score ED_2b_score OP_18b_score
# 2514 3294 1222 1238 1234
# OP_20_score OP_21_score OP_3b_score OP_5_score CAC_3_score
# 1223 1505 4425 2574 4643
# IMM_2_score IMM_3_OP_27_FAC_ADHPCT_score OP_22_score OP_23_score OP_29_score
# 1034 711 1543 3608 2087
# OP_30_score OP_4_score PC_01_score STK_4_score STK_5_score
# 2191 2599 2296 3919 3276
# STK_6_score STK_8_score VTE_1_score VTE_2_score VTE_3_score
# 2239 2454 1200 1883 2332
# VTE_5_score VTE_6_score
# 2587 3560
# >
sum(sapply(master_data_x, function(x) sum(length(which(is.na(x))))))
## [1] 131127
# [1] 131127
## The Score columns are having 131127 NA values, score columns are very important for our further analysis. As these are all independent(X) variables
## we will deal with the NA cleaning after merging with Dependent variable (ratings- y) in later phase
str(master_data_x)
## 'data.frame': 4818 obs. of 72 variables:
## $ Provider.ID : int 100001 100002 100006 100007 100008 100009 10001 100012 100014 100017 ...
## $ Hospital.Name : chr "UF HEALTH JACKSONVILLE" "BETHESDA HOSPITAL EAST" "ORLANDO HEALTH" "FLORIDA HOSPITAL" ...
## $ Address : chr "655 W 8TH ST" "2815 S SEACREST BLVD" "52 W UNDERWOOD ST" "601 E ROLLINS ST" ...
## $ City : chr "JACKSONVILLE" "BOYNTON BEACH" "ORLANDO" "ORLANDO" ...
## $ State : chr "FL" "FL" "FL" "FL" ...
## $ ZIP.Code : int 32209 33435 32806 32803 33176 33136 36301 33901 32170 32114 ...
## $ County.Name : chr "DUVAL" "PALM BEACH" "ORANGE" "ORANGE" ...
## $ Phone.Number : num 9.04e+09 5.62e+09 3.22e+09 4.07e+09 7.87e+09 ...
## $ READM_30_AMI_score : num -2.618 0.722 0.304 -0.948 1.348 ...
## $ READM_30_CABG_score : num -1.946 0.627 0.805 -2.39 0.716 ...
## $ READM_30_COPD_score : num -0.1581 -0.5518 0.0782 -2.9142 0.3144 ...
## $ READM_30_HF_score : num -1.967 -0.364 1.305 -2.034 -0.431 ...
## $ READM_30_HIP_KNEE_score : num -0.703 1.284 -0.342 -1.426 1.103 ...
## $ READM_30_HOSP_WIDE_score : num -1.963 0.457 -0.511 -3.658 0.821 ...
## $ READM_30_PN_score : num -1.245 0.426 0.287 -1.593 -0.479 ...
## $ READM_30_STK_score : num -1.15 1.367 -0.777 -2.175 -0.87 ...
## $ MORT_30_AMI_score : num -2.504 1.489 -0.907 -0.667 0.451 ...
## $ MORT_30_CABG_score : num -0.308 -0.193 0.495 -2.257 1.068 ...
## $ MORT_30_COPD_score : num -0.0154 -0.2851 0.8833 1.7821 1.6024 ...
## $ MORT_30_HF_score : num 1.2026 0.1765 -0.0971 0.929 0.7922 ...
## $ MORT_30_PN_score : num 1.335 0.762 0.332 0.475 1.717 ...
## $ MORT_30_STK_score : num -1.9646 0.4992 0.0185 1.7612 1.3405 ...
## $ PSI_4_SURG_COMP_score : num -1.068 1.544 -0.49 0.998 0.144 ...
## $ COMP_HIP_KNEE_score : num -0.104 1.325 0.432 0.789 1.504 ...
## $ HAI_1_SIR_score : num -3.889 0.133 0.274 0.421 -0.492 ...
## $ HAI_2_SIR_score : num -0.4762 0.0302 -0.5797 0.1117 0.0236 ...
## $ HAI_3_SIR_score : num 0.087 -0.379 0.658 -0.216 0.237 ...
## $ HAI_4_SIR_score : num -2.088 0.228 0.309 0.488 -2.129 ...
## $ HAI_5_SIR_score : num -3.193 -1.519 0.639 0.287 0.15 ...
## $ HAI_6_SIR_score : num -1.204 0.395 -0.394 0.156 0.179 ...
## $ PSI_90_SAFETY_score : num -1.557 0.346 0.288 -0.635 -1.384 ...
## $ H_CLEAN_LINEAR_SCORE : num -1.888 -1.112 0.183 0.701 0.183 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.918 -0.918 -0.129 0.265 0.265 ...
## $ H_COMP_2_LINEAR_SCORE : num -0.777 -1.596 -0.368 -0.777 0.45 ...
## $ H_COMP_3_LINEAR_SCORE : num -0.9724 -1.4273 -0.29 -0.0626 -0.29 ...
## $ H_COMP_4_LINEAR_SCORE : num -1.379 -0.22 0.167 0.167 0.167 ...
## $ H_COMP_5_LINEAR_SCORE : num -0.4152 -1.3482 -0.182 0.5178 0.0513 ...
## $ H_COMP_6_LINEAR_SCORE : num -0.534 -1.939 -0.815 0.309 -0.253 ...
## $ H_COMP_7_LINEAR_SCORE : num -0.532 -0.532 0.165 0.513 0.861 ...
## $ H_HSP_RATING_LINEAR_SCORE : num -0.8378 -0.2231 0.0842 0.3915 0.3915 ...
## $ H_QUIET_LINEAR_SCORE : num -0.59675 -0.00976 0.38156 0.57722 0.38156 ...
## $ H_RECMND_LINEAR_SCORE : num -1.151 -0.236 0.221 0.679 0.908 ...
## $ OP_10_score : num -2.602 0.73 0.554 0.495 0.73 ...
## $ OP_11_score : num -2.197 0.423 0.474 0.474 0.44 ...
## $ OP_13_score : num 1.1862 NA 0.0925 -0.5537 -1.7468 ...
## $ OP_14_score : num 0.0469 -1.1843 -0.756 -0.3813 0.3681 ...
## $ OP_8_score : num 1.295 NA NA -0.349 NA ...
## $ ED_1b_score : num -1.735 0.215 -0.503 -0.629 -1.851 ...
## $ ED_2b_score : num -1.312 -0.478 -0.235 -0.281 -2.086 ...
## $ OP_18b_score : num -1.766 -0.123 -0.551 -1.075 -1.932 ...
## $ OP_20_score : num -0.609 0.76 -0.547 -0.36 -1.854 ...
## $ OP_21_score : num -0.946 -0.153 -0.55 -1.512 0.696 ...
## $ OP_3b_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ OP_5_score : num -5.14 NA NA NA NA ...
## $ CAC_3_score : num NA NA 0.606 NA NA ...
## $ IMM_2_score : num 0.281 0.53 0.197 0.53 0.613 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num 0.8395 -1.5068 0.0351 -0.3672 0.1691 ...
## $ OP_22_score : num -3.387 0.438 -0.108 -0.108 -0.108 ...
## $ OP_23_score : num -0.777 NA -0.777 -2.136 NA ...
## $ OP_29_score : num 0.981 -1.184 0.764 -1.364 -1.148 ...
## $ OP_30_score : num 0.844 0.2 0.629 -0.315 0.371 ...
## $ OP_4_score : num -2.48 NA NA NA NA ...
## $ PC_01_score : num 0.539 0.539 -0.75 0.539 0.539 ...
## $ STK_4_score : num 0.153 -0.179 0.374 0.374 0.594 ...
## $ STK_5_score : num -0.243 0.1932 -0.0249 -0.0249 0.4114 ...
## $ STK_6_score : num 0.446 -0.297 0.57 0.322 0.446 ...
## $ STK_8_score : num -0.113 0.618 0.253 0.435 0.618 ...
## $ VTE_1_score : num 0.334 0.489 0.412 0.412 0.567 ...
## $ VTE_2_score : num -0.118 -0.45 0.547 0.381 0.547 ...
## $ VTE_3_score : num -0.643 0.332 0.21 -1.253 0.819 ...
## $ VTE_5_score : num 0.635 0.369 0.191 0.102 0.635 ...
## $ VTE_6_score : num 0.416 0.416 0.212 0.212 0.416 ...
hospital_ratings <- read.csv("Hospital General Information.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
dim(hospital_ratings) ## 4818 rows and 28 columns - For each hospital one row is existing
## [1] 4818 28
# [1] 4818 28
str(hospital_ratings)
## 'data.frame': 4818 obs. of 28 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ Hospital.Type : chr "Acute Care Hospitals" "Acute Care Hospitals" "Acute Care Hospitals" "Acute Care Hospitals" ...
## $ Hospital.Ownership : chr "Government - Hospital District or Authority" "Government - Hospital District or Authority" "Government - Hospital District or Authority" "Voluntary non-profit - Private" ...
## $ Emergency.Services : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Meets.criteria.for.meaningful.use.of.EHRs : chr "Y" "Y" "Y" "Y" ...
## $ Hospital.overall.rating : int 3 3 2 3 3 2 3 3 NA 2 ...
## $ Hospital.overall.rating.footnote : chr "" "" "" "" ...
## $ Mortality.national.comparison : chr "Same as the National average" "Below the National average" "Below the National average" "Same as the National average" ...
## $ Mortality.national.comparison.footnote : chr "" "" "" "" ...
## $ Safety.of.care.national.comparison : chr "Above the National average" "Same as the National average" "Same as the National average" "Same as the National average" ...
## $ Safety.of.care.national.comparison.footnote : chr "" "" "" "" ...
## $ Readmission.national.comparison : chr "Same as the National average" "Above the National average" "Same as the National average" "Below the National average" ...
## $ Readmission.national.comparison.footnote : chr "" "" "" "" ...
## $ Patient.experience.national.comparison : chr "Below the National average" "Same as the National average" "Below the National average" "Same as the National average" ...
## $ Patient.experience.national.comparison.footnote : chr "" "" "" "" ...
## $ Effectiveness.of.care.national.comparison : chr "Same as the National average" "Same as the National average" "Same as the National average" "Same as the National average" ...
## $ Effectiveness.of.care.national.comparison.footnote : chr "" "" "" "" ...
## $ Timeliness.of.care.national.comparison : chr "Same as the National average" "Above the National average" "Above the National average" "Above the National average" ...
## $ Timeliness.of.care.national.comparison.footnote : chr "" "" "" "" ...
## $ Efficient.use.of.medical.imaging.national.comparison : chr "Same as the National average" "Below the National average" "Same as the National average" NA ...
## $ Efficient.use.of.medical.imaging.national.comparison.footnote: chr "" "" "" "Results are not available for this reporting period" ...
master_data_y <- hospital_ratings
sum(is.na(hospital_ratings$Hospital.overall.rating)) ## 321 records having NA values
## [1] 1170
# [1] 1170
# We need only the Provide.ID and Hospital.overall.rating columns
master_data_y <- master_data_y[, c(1, 13)]
str(master_data_y)
## 'data.frame': 4818 obs. of 2 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.overall.rating: int 3 3 2 3 3 2 3 3 NA 2 ...
dim(master_data_y) ## 4818 rows and 2 columns (Provider id , Hospital CMS rating)
## [1] 4818 2
# [1] 4818 2
## Merge X (independent) & Y (dependent) variables to one using Provider id
dim(master_data_x)
## [1] 4818 72
# [1] 4818 72
master_data <- merge(master_data_x, master_data_y, by = "Provider.ID")
dim(master_data) ## 4818 rows & 73 columns
## [1] 4818 73
#[1] 4818 73
## Remove all the record having NA in Hospital.overall.rating column
sum(is.na(master_data$Hospital.overall.rating)) ## 1170 records having NA values
## [1] 1170
# [1] 1170
master_data_with_na <- master_data[is.na(master_data$Hospital.overall.rating),]
master_data_without_na <- master_data[!is.na(master_data$Hospital.overall.rating),]
dim(master_data_without_na) ## 3648 rows & 73 columns (4818 total records)
## [1] 3648 73
# [1] 3648 73
dim(master_data_with_na) ## 1170 rows and 73 columns
## [1] 1170 73
# [1] 1170 73
## NA values across all coulumns
sapply(master_data_without_na, function(x) sum(is.na(x)))
## Provider.ID Hospital.Name
## 0 0
## Address City
## 0 0
## State ZIP.Code
## 0 0
## County.Name Phone.Number
## 0 0
## READM_30_AMI_score READM_30_CABG_score
## 1490 2623
## READM_30_COPD_score READM_30_HF_score
## 231 207
## READM_30_HIP_KNEE_score READM_30_HOSP_WIDE_score
## 992 3
## READM_30_PN_score READM_30_STK_score
## 125 1049
## MORT_30_AMI_score MORT_30_CABG_score
## 1267 2612
## MORT_30_COPD_score MORT_30_HF_score
## 257 221
## MORT_30_PN_score MORT_30_STK_score
## 131 984
## PSI_4_SURG_COMP_score COMP_HIP_KNEE_score
## 1831 1007
## HAI_1_SIR_score HAI_2_SIR_score
## 1322 818
## HAI_3_SIR_score HAI_4_SIR_score
## 1642 2804
## HAI_5_SIR_score HAI_6_SIR_score
## 1860 497
## PSI_90_SAFETY_score H_CLEAN_LINEAR_SCORE
## 605 273
## H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE
## 273 273
## H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 273 273
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
## 273 273
## H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE
## 273 273
## H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 273 273
## OP_10_score OP_11_score
## 287 455
## OP_13_score OP_14_score
## 1429 1438
## OP_8_score ED_1b_score
## 2141 347
## ED_2b_score OP_18b_score
## 350 381
## OP_20_score OP_21_score
## 376 497
## OP_3b_score OP_5_score
## 3267 1582
## CAC_3_score IMM_2_score
## 3516 242
## IMM_3_OP_27_FAC_ADHPCT_score OP_22_score
## 149 546
## OP_23_score OP_29_score
## 2467 1008
## OP_30_score OP_4_score
## 1095 1606
## PC_01_score STK_4_score
## 1203 2762
## STK_5_score STK_6_score
## 2133 1102
## STK_8_score VTE_1_score
## 1315 327
## VTE_2_score VTE_3_score
## 778 1208
## VTE_5_score VTE_6_score
## 1458 2417
## Hospital.overall.rating
## 0
dim(master_data_without_na) ## 3648 rows and 73 columns
## [1] 3648 73
# [1] 3648 73
# Remove all columns having more than 50% NA in the dataset .. which are not going to yield any outcome
# which is close to 1824 NA values in any x measure.. will remove the measure
## The following measures having >50% of its data as NA
## READM_30_CABG - 2623 | PSI_4_SURG_COMP - 1831 | HAI_4_SIR - 2804 | HAI_5_SIR - 1860 | OP_3b - 3267 |
## STK_4 - 2762 | STK_5 - 2133 | VTE_6 - 2417
output <- names(which(sapply(master_data_without_na, function(x) sum(is.na(x)) < 1824)))
output
## [1] "Provider.ID" "Hospital.Name"
## [3] "Address" "City"
## [5] "State" "ZIP.Code"
## [7] "County.Name" "Phone.Number"
## [9] "READM_30_AMI_score" "READM_30_COPD_score"
## [11] "READM_30_HF_score" "READM_30_HIP_KNEE_score"
## [13] "READM_30_HOSP_WIDE_score" "READM_30_PN_score"
## [15] "READM_30_STK_score" "MORT_30_AMI_score"
## [17] "MORT_30_COPD_score" "MORT_30_HF_score"
## [19] "MORT_30_PN_score" "MORT_30_STK_score"
## [21] "COMP_HIP_KNEE_score" "HAI_1_SIR_score"
## [23] "HAI_2_SIR_score" "HAI_3_SIR_score"
## [25] "HAI_6_SIR_score" "PSI_90_SAFETY_score"
## [27] "H_CLEAN_LINEAR_SCORE" "H_COMP_1_LINEAR_SCORE"
## [29] "H_COMP_2_LINEAR_SCORE" "H_COMP_3_LINEAR_SCORE"
## [31] "H_COMP_4_LINEAR_SCORE" "H_COMP_5_LINEAR_SCORE"
## [33] "H_COMP_6_LINEAR_SCORE" "H_COMP_7_LINEAR_SCORE"
## [35] "H_HSP_RATING_LINEAR_SCORE" "H_QUIET_LINEAR_SCORE"
## [37] "H_RECMND_LINEAR_SCORE" "OP_10_score"
## [39] "OP_11_score" "OP_13_score"
## [41] "OP_14_score" "ED_1b_score"
## [43] "ED_2b_score" "OP_18b_score"
## [45] "OP_20_score" "OP_21_score"
## [47] "OP_5_score" "IMM_2_score"
## [49] "IMM_3_OP_27_FAC_ADHPCT_score" "OP_22_score"
## [51] "OP_29_score" "OP_30_score"
## [53] "OP_4_score" "PC_01_score"
## [55] "STK_6_score" "STK_8_score"
## [57] "VTE_1_score" "VTE_2_score"
## [59] "VTE_3_score" "VTE_5_score"
## [61] "Hospital.overall.rating"
master_data_without_na <- master_data_without_na[, output]
str(master_data_without_na)
## 'data.frame': 3648 obs. of 61 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10019 10021 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35660 36360 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ READM_30_AMI_score : num 0.409 0.2 0.826 NA NA ...
## $ READM_30_COPD_score : num -0.8668 1.5743 0.1569 0.0782 0.6294 ...
## $ READM_30_HF_score : num 0.3704 0.0364 0.9047 0.5707 -0.765 ...
## $ READM_30_HIP_KNEE_score : num -0.884 -1.968 -0.703 NA NA ...
## $ READM_30_HOSP_WIDE_score : num 0.215 0.821 0.215 -1.237 -0.148 ...
## $ READM_30_PN_score : num -1.105 0.496 -0.549 -0.131 0.774 ...
## $ READM_30_STK_score : num -0.125 -0.777 0.528 -0.125 NA ...
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.1 NA NA ...
## $ MORT_30_COPD_score : num -1.094 0.434 0.883 -1.094 -0.105 ...
## $ MORT_30_HF_score : num -0.166 -2.286 -2.355 -1.534 -0.371 ...
## $ MORT_30_PN_score : num 0.428 -2.103 -0.862 -1.148 0.332 ...
## $ MORT_30_STK_score : num -0.282 -0.342 -1.784 -1.003 NA ...
## $ COMP_HIP_KNEE_score : num -1.355 0.0745 -1.355 NA NA ...
## $ HAI_1_SIR_score : num -2.351 -1.023 0.389 NA NA ...
## $ HAI_2_SIR_score : num -2.088 0.05 -0.357 1.054 NA ...
## $ HAI_3_SIR_score : num -1.135 0.723 0.818 NA NA ...
## $ HAI_6_SIR_score : num 0.0566 0.7984 0.5887 1.5849 0.4489 ...
## $ PSI_90_SAFETY_score : num 1.2108 0.2304 -0.1156 0.5764 -0.0579 ...
## $ H_CLEAN_LINEAR_SCORE : num -0.853 -1.112 -1.112 0.442 NA ...
## $ H_COMP_1_LINEAR_SCORE : num -0.524 -0.129 -0.129 -0.129 NA ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0412 0.8596 0.8596 1.678 NA ...
## $ H_COMP_3_LINEAR_SCORE : num -1.2 -0.29 -0.517 0.392 NA ...
## $ H_COMP_4_LINEAR_SCORE : num -0.606 0.167 -0.22 0.553 NA ...
## $ H_COMP_5_LINEAR_SCORE : num -0.415 0.285 -0.182 0.751 NA ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0276 0.3087 -1.0964 -0.2534 NA ...
## $ H_COMP_7_LINEAR_SCORE : num 0.165 -0.183 -0.532 0.165 NA ...
## $ H_HSP_RATING_LINEAR_SCORE : num 0.0842 0.3915 -1.1451 -0.5304 NA ...
## $ H_QUIET_LINEAR_SCORE : num 0.969 0.577 0.577 1.751 NA ...
## $ H_RECMND_LINEAR_SCORE : num 0.45 0.221 -0.923 -0.465 NA ...
## $ OP_10_score : num 0.251 -0.423 -0.277 -1.498 0.524 ...
## $ OP_11_score : num 0.389 -1.204 -0.245 -0.502 NA ...
## $ OP_13_score : num -1.2 -0.305 2.33 NA NA ...
## $ OP_14_score : num 0.207 -0.649 -0.97 NA 1.171 ...
## $ ED_1b_score : num 0.0892 0.3415 0.5938 0.5743 0.9527 ...
## $ ED_2b_score : num 0.508 0.463 0.357 0.508 0.69 ...
## $ OP_18b_score : num -1.266 0.616 0.235 0.568 1.068 ...
## $ OP_20_score : num -2.414 -0.049 1.009 -0.733 -0.049 ...
## $ OP_21_score : num -2.587 -0.38 -0.267 -2.078 0.243 ...
## $ OP_5_score : num NA -0.714 NA 0.248 NA ...
## $ IMM_2_score : num 0.364 0.53 0.613 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.233 -0.099 0.169 -2.11 -2.781 ...
## $ OP_22_score : num -1.201 -0.108 0.438 0.438 0.438 ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.546 NA -1.232 NA ...
## $ PC_01_score : num 0.539 0.324 0.539 NA NA ...
## $ STK_6_score : num 0.446 -0.915 0.198 -3.019 NA ...
## $ STK_8_score : num -0.57 0.618 -0.296 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.381 -0.45 -1.282 0.381 NA ...
## $ VTE_3_score : num -0.278 0.819 -0.887 NA NA ...
## $ VTE_5_score : num -0.2536 0.6352 -0.0759 NA NA ...
## $ Hospital.overall.rating : int 3 3 2 3 3 2 3 3 2 4 ...
## Replace NA with median values of the measure
names(master_data_without_na)
## [1] "Provider.ID" "Hospital.Name"
## [3] "Address" "City"
## [5] "State" "ZIP.Code"
## [7] "County.Name" "Phone.Number"
## [9] "READM_30_AMI_score" "READM_30_COPD_score"
## [11] "READM_30_HF_score" "READM_30_HIP_KNEE_score"
## [13] "READM_30_HOSP_WIDE_score" "READM_30_PN_score"
## [15] "READM_30_STK_score" "MORT_30_AMI_score"
## [17] "MORT_30_COPD_score" "MORT_30_HF_score"
## [19] "MORT_30_PN_score" "MORT_30_STK_score"
## [21] "COMP_HIP_KNEE_score" "HAI_1_SIR_score"
## [23] "HAI_2_SIR_score" "HAI_3_SIR_score"
## [25] "HAI_6_SIR_score" "PSI_90_SAFETY_score"
## [27] "H_CLEAN_LINEAR_SCORE" "H_COMP_1_LINEAR_SCORE"
## [29] "H_COMP_2_LINEAR_SCORE" "H_COMP_3_LINEAR_SCORE"
## [31] "H_COMP_4_LINEAR_SCORE" "H_COMP_5_LINEAR_SCORE"
## [33] "H_COMP_6_LINEAR_SCORE" "H_COMP_7_LINEAR_SCORE"
## [35] "H_HSP_RATING_LINEAR_SCORE" "H_QUIET_LINEAR_SCORE"
## [37] "H_RECMND_LINEAR_SCORE" "OP_10_score"
## [39] "OP_11_score" "OP_13_score"
## [41] "OP_14_score" "ED_1b_score"
## [43] "ED_2b_score" "OP_18b_score"
## [45] "OP_20_score" "OP_21_score"
## [47] "OP_5_score" "IMM_2_score"
## [49] "IMM_3_OP_27_FAC_ADHPCT_score" "OP_22_score"
## [51] "OP_29_score" "OP_30_score"
## [53] "OP_4_score" "PC_01_score"
## [55] "STK_6_score" "STK_8_score"
## [57] "VTE_1_score" "VTE_2_score"
## [59] "VTE_3_score" "VTE_5_score"
## [61] "Hospital.overall.rating"
meas <- names(master_data_without_na[, 9:ncol(master_data_without_na)])
for (i in meas) {
print(i)
print(median(master_data_without_na[, i], na.rm = TRUE))
med <- median(master_data_without_na[, i], na.rm = TRUE)
master_data_without_na[i][is.na(master_data_without_na[i])] = med
}
## [1] "READM_30_AMI_score"
## [1] 0.09557352
## [1] "READM_30_COPD_score"
## [1] 0.07816308
## [1] "READM_30_HF_score"
## [1] 0.03644808
## [1] "READM_30_HIP_KNEE_score"
## [1] 0.01938441
## [1] "READM_30_HOSP_WIDE_score"
## [1] 0.09434903
## [1] "READM_30_PN_score"
## [1] 0.07794659
## [1] "READM_30_STK_score"
## [1] 0.0618616
## [1] "MORT_30_AMI_score"
## [1] 0.05139874
## [1] "MORT_30_COPD_score"
## [1] 0.07443538
## [1] "MORT_30_HF_score"
## [1] 0.03968628
## [1] "MORT_30_PN_score"
## [1] 0.04560634
## [1] "MORT_30_STK_score"
## [1] 0.0785529
## [1] "COMP_HIP_KNEE_score"
## [1] 0.07446038
## [1] "HAI_1_SIR_score"
## [1] 0.1494836
## [1] "HAI_2_SIR_score"
## [1] 0.1270887
## [1] "HAI_3_SIR_score"
## [1] 0.1595961
## [1] "HAI_6_SIR_score"
## [1] 0.02362961
## [1] "PSI_90_SAFETY_score"
## [1] 0.1150839
## [1] "H_CLEAN_LINEAR_SCORE"
## [1] -0.07594687
## [1] "H_COMP_1_LINEAR_SCORE"
## [1] 0.2652014
## [1] "H_COMP_2_LINEAR_SCORE"
## [1] 0.04117737
## [1] "H_COMP_3_LINEAR_SCORE"
## [1] -0.06257106
## [1] "H_COMP_4_LINEAR_SCORE"
## [1] 0.1667196
## [1] "H_COMP_5_LINEAR_SCORE"
## [1] 0.05126318
## [1] "H_COMP_6_LINEAR_SCORE"
## [1] 0.02763704
## [1] "H_COMP_7_LINEAR_SCORE"
## [1] 0.1648826
## [1] "H_HSP_RATING_LINEAR_SCORE"
## [1] 0.08418745
## [1] "H_QUIET_LINEAR_SCORE"
## [1] -0.009760763
## [1] "H_RECMND_LINEAR_SCORE"
## [1] -0.007499389
## [1] "OP_10_score"
## [1] 0.3191332
## [1] "OP_11_score"
## [1] 0.3886441
## [1] "OP_13_score"
## [1] 0.0428105
## [1] "OP_14_score"
## [1] 0.1004377
## [1] "ED_1b_score"
## [1] 0.1571615
## [1] "ED_2b_score"
## [1] 0.2048344
## [1] "OP_18b_score"
## [1] 0.04420063
## [1] "OP_20_score"
## [1] 0.1999376
## [1] "OP_21_score"
## [1] 0.07301241
## [1] "OP_5_score"
## [1] 0.2479597
## [1] "IMM_2_score"
## [1] 0.363809
## [1] "IMM_3_OP_27_FAC_ADHPCT_score"
## [1] 0.3032114
## [1] "OP_22_score"
## [1] 0.4381978
## [1] "OP_29_score"
## [1] 0.3673624
## [1] "OP_30_score"
## [1] 0.4143775
## [1] "OP_4_score"
## [1] 0.3679448
## [1] "PC_01_score"
## [1] 0.3239793
## [1] "STK_6_score"
## [1] 0.3221672
## [1] "STK_8_score"
## [1] 0.3440135
## [1] "VTE_1_score"
## [1] 0.3337948
## [1] "VTE_2_score"
## [1] 0.3811109
## [1] "VTE_3_score"
## [1] 0.3318061
## [1] "VTE_5_score"
## [1] 0.3685471
## [1] "Hospital.overall.rating"
## [1] 3
## check for NA values after replacing NA with Median
sapply(master_data_without_na, function(x) sum(is.na(x)))
## Provider.ID Hospital.Name
## 0 0
## Address City
## 0 0
## State ZIP.Code
## 0 0
## County.Name Phone.Number
## 0 0
## READM_30_AMI_score READM_30_COPD_score
## 0 0
## READM_30_HF_score READM_30_HIP_KNEE_score
## 0 0
## READM_30_HOSP_WIDE_score READM_30_PN_score
## 0 0
## READM_30_STK_score MORT_30_AMI_score
## 0 0
## MORT_30_COPD_score MORT_30_HF_score
## 0 0
## MORT_30_PN_score MORT_30_STK_score
## 0 0
## COMP_HIP_KNEE_score HAI_1_SIR_score
## 0 0
## HAI_2_SIR_score HAI_3_SIR_score
## 0 0
## HAI_6_SIR_score PSI_90_SAFETY_score
## 0 0
## H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## 0 0
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE
## 0 0
## H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE
## 0 0
## H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## 0 0
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE
## 0 0
## H_RECMND_LINEAR_SCORE OP_10_score
## 0 0
## OP_11_score OP_13_score
## 0 0
## OP_14_score ED_1b_score
## 0 0
## ED_2b_score OP_18b_score
## 0 0
## OP_20_score OP_21_score
## 0 0
## OP_5_score IMM_2_score
## 0 0
## IMM_3_OP_27_FAC_ADHPCT_score OP_22_score
## 0 0
## OP_29_score OP_30_score
## 0 0
## OP_4_score PC_01_score
## 0 0
## STK_6_score STK_8_score
## 0 0
## VTE_1_score VTE_2_score
## 0 0
## VTE_3_score VTE_5_score
## 0 0
## Hospital.overall.rating
## 0
# No NA's found.
dim(master_data_without_na)
## [1] 3648 61
# [1] 3648 61
str(master_data_without_na)
## 'data.frame': 3648 obs. of 61 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10019 10021 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35660 36360 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ READM_30_AMI_score : num 0.4086 0.1999 0.8261 0.0956 0.0956 ...
## $ READM_30_COPD_score : num -0.8668 1.5743 0.1569 0.0782 0.6294 ...
## $ READM_30_HF_score : num 0.3704 0.0364 0.9047 0.5707 -0.765 ...
## $ READM_30_HIP_KNEE_score : num -0.884 -1.9681 -0.7033 0.0194 0.0194 ...
## $ READM_30_HOSP_WIDE_score : num 0.215 0.821 0.215 -1.237 -0.148 ...
## $ READM_30_PN_score : num -1.105 0.496 -0.549 -0.131 0.774 ...
## $ READM_30_STK_score : num -0.1245 -0.777 0.5279 -0.1245 0.0619 ...
## $ MORT_30_AMI_score : num 1.2493 -1.5457 -2.1047 0.0514 0.0514 ...
## $ MORT_30_COPD_score : num -1.094 0.434 0.883 -1.094 -0.105 ...
## $ MORT_30_HF_score : num -0.166 -2.286 -2.355 -1.534 -0.371 ...
## $ MORT_30_PN_score : num 0.428 -2.103 -0.862 -1.148 0.332 ...
## $ MORT_30_STK_score : num -0.282 -0.3421 -1.7844 -1.0031 0.0786 ...
## $ COMP_HIP_KNEE_score : num -1.355 0.0745 -1.355 0.0745 0.0745 ...
## $ HAI_1_SIR_score : num -2.351 -1.023 0.389 0.149 0.149 ...
## $ HAI_2_SIR_score : num -2.088 0.05 -0.357 1.054 0.127 ...
## $ HAI_3_SIR_score : num -1.135 0.723 0.818 0.16 0.16 ...
## $ HAI_6_SIR_score : num 0.0566 0.7984 0.5887 1.5849 0.4489 ...
## $ PSI_90_SAFETY_score : num 1.2108 0.2304 -0.1156 0.5764 -0.0579 ...
## $ H_CLEAN_LINEAR_SCORE : num -0.8527 -1.1116 -1.1116 0.4419 -0.0759 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.524 -0.129 -0.129 -0.129 0.265 ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0412 0.8596 0.8596 1.678 0.0412 ...
## $ H_COMP_3_LINEAR_SCORE : num -1.1999 -0.29 -0.5175 0.3923 -0.0626 ...
## $ H_COMP_4_LINEAR_SCORE : num -0.606 0.167 -0.22 0.553 0.167 ...
## $ H_COMP_5_LINEAR_SCORE : num -0.4152 0.2845 -0.182 0.751 0.0513 ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0276 0.3087 -1.0964 -0.2534 0.0276 ...
## $ H_COMP_7_LINEAR_SCORE : num 0.165 -0.183 -0.532 0.165 0.165 ...
## $ H_HSP_RATING_LINEAR_SCORE : num 0.0842 0.3915 -1.1451 -0.5304 0.0842 ...
## $ H_QUIET_LINEAR_SCORE : num 0.96855 0.57722 0.57722 1.75119 -0.00976 ...
## $ H_RECMND_LINEAR_SCORE : num 0.45 0.2213 -0.9226 -0.465 -0.0075 ...
## $ OP_10_score : num 0.251 -0.423 -0.277 -1.498 0.524 ...
## $ OP_11_score : num 0.389 -1.204 -0.245 -0.502 0.389 ...
## $ OP_13_score : num -1.2 -0.3052 2.3296 0.0428 0.0428 ...
## $ OP_14_score : num 0.207 -0.649 -0.97 0.1 1.171 ...
## $ ED_1b_score : num 0.0892 0.3415 0.5938 0.5743 0.9527 ...
## $ ED_2b_score : num 0.508 0.463 0.357 0.508 0.69 ...
## $ OP_18b_score : num -1.266 0.616 0.235 0.568 1.068 ...
## $ OP_20_score : num -2.414 -0.049 1.009 -0.733 -0.049 ...
## $ OP_21_score : num -2.587 -0.38 -0.267 -2.078 0.243 ...
## $ OP_5_score : num 0.248 -0.714 0.248 0.248 0.248 ...
## $ IMM_2_score : num 0.364 0.53 0.613 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.233 -0.099 0.169 -2.11 -2.781 ...
## $ OP_22_score : num -1.201 -0.108 0.438 0.438 0.438 ...
## $ OP_29_score : num 0.367 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num 0.368 0.546 0.368 -1.232 0.368 ...
## $ PC_01_score : num 0.539 0.324 0.539 0.324 0.324 ...
## $ STK_6_score : num 0.446 -0.915 0.198 -3.019 0.322 ...
## $ STK_8_score : num -0.57 0.618 -0.296 0.344 0.344 ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.381 -0.45 -1.282 0.381 0.381 ...
## $ VTE_3_score : num -0.278 0.819 -0.887 0.332 0.332 ...
## $ VTE_5_score : num -0.2536 0.6352 -0.0759 0.3685 0.3685 ...
## $ Hospital.overall.rating : int 3 3 2 3 3 2 3 3 2 4 ...
## Remove hospital information which is not required
master_data_without_na <- master_data_without_na[, -c(2:8)]
master_data_without_na$Hospital.overall.rating <- as.factor(master_data_without_na$Hospital.overall.rating)
cleaned_master_data <- master_data_without_na
dim(cleaned_master_data)
## [1] 3648 54
# [1] 3648 54
Perform EDA
library(Information)
# let us check the hospital_ratings dataset
summary(factor(hospital_ratings$Hospital.Type))
## Acute Care Hospitals Childrens
## 3382 99
## Critical Access Hospitals
## 1337
# Acute Care Hospitals Childrens Critical Access Hospitals
# 3382 99 1337
# Acute care hospitals are the highest in the dataset, followed by Critical Access Hospitals and Childrens hospitals.
# Let us verify the hospitals ratings for each type for hospitals.
table(hospital_ratings$Hospital.Type, hospital_ratings$Hospital.overall.rating)
##
## 1 2 3 4 5
## Acute Care Hospitals 117 659 1426 749 110
## Childrens 0 0 0 0 0
## Critical Access Hospitals 0 25 346 215 1
# 1 2 3 4 5
# Acute Care Hospitals 117 659 1426 749 110
# Childrens 0 0 0 0 0
# Critical Access Hospitals 0 25 346 215 1
# Since the Childrens hospital has no ratings, we will filter these records from the dataset.
# we will remove the Critical Access Hospitals as well, as there are no ratings for 1 and very few for 5.
hospital_ratings <- filter(hospital_ratings, Hospital.Type == "Acute Care Hospitals")
hospital_ratings$Hospital.Type <- factor(as.character(hospital_ratings$Hospital.Type))
table(hospital_ratings$Hospital.Type, hospital_ratings$Hospital.overall.rating)
##
## 1 2 3 4 5
## Acute Care Hospitals 117 659 1426 749 110
# 1 2 3 4 5
# Acute Care Hospitals 117 659 1426 749 110
# Let us check the Overall rating
unique(hospital_ratings$Hospital.overall.rating)
## [1] 3 2 NA 4 5 1
# [1] 3 2 NA 4 5 1
hospital_ratings$Hospital.overall.rating <- func_numeric(hospital_ratings$Hospital.overall.rating)
# Let's check the Ownership type for the hospitals.
table(hospital_ratings$Hospital.Ownership)
##
## Government - Federal
## 38
## Government - Hospital District or Authority
## 281
## Government - Local
## 183
## Government - State
## 54
## Physician
## 63
## Proprietary
## 723
## Tribal
## 4
## Voluntary non-profit - Church
## 284
## Voluntary non-profit - Other
## 369
## Voluntary non-profit - Private
## 1383
# Government - Federal Government - Hospital District or Authority Government - Local
# 38 281 183
# Government - State Physician Proprietary
# 54 63 723
# Tribal Voluntary non-profit - Church Voluntary non-profit - Other
# 4 284 369
# Voluntary non-profit - Private
# 1383
# We see the Private hospitals have the data regarding the ratings in the dataset.
# Let us remove the NA's values-
hospital_ratings <- hospital_ratings[!is.na(hospital_ratings$Hospital.overall.rating),]
dim(hospital_ratings)
## [1] 3061 28
# [1] 3061 28
# Let us Segment across different groups categorical variables and compare their average overall ratings
avg_by_state <- hospital_ratings %>%
group_by(State) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), provider_count = n()) %>%
arrange(desc(avg_rating))
avg_by_state
## # A tibble: 53 x 3
## State avg_rating provider_count
## <chr> <dbl> <int>
## 1 SD 4.2 15
## 2 WI 3.69 65
## 3 DE 3.67 6
## 4 ID 3.67 12
## 5 IN 3.59 80
## 6 MT 3.58 12
## 7 NH 3.54 13
## 8 KS 3.52 44
## 9 MN 3.52 48
## 10 CO 3.5 44
## # … with 43 more rows
head(avg_by_state, 10)
## # A tibble: 10 x 3
## State avg_rating provider_count
## <chr> <dbl> <int>
## 1 SD 4.2 15
## 2 WI 3.69 65
## 3 DE 3.67 6
## 4 ID 3.67 12
## 5 IN 3.59 80
## 6 MT 3.58 12
## 7 NH 3.54 13
## 8 KS 3.52 44
## 9 MN 3.52 48
## 10 CO 3.5 44
# A tibble: 53 x 3
# State avg_rating provider_count
# <chr> <dbl> <int>
# SD 4.2 15
# WI 3.69 65
# DE 3.67 6
# ID 3.67 12
# IN 3.59 80
# MT 3.58 12
# NH 3.54 13
# KS 3.52 44
# MN 3.52 48
# CO 3.5 44
# We see that South Dakota(SD) has the highest average of 4.2 but the frequency is not the highest.
# we see that Indiana has an average of 3.59 and has the highest frequency.
# We see that Wisconsin has an average of 3.69 and a good count of providers.
graph_by_prov_state <- head(avg_by_state, 10) %>%
ggplot(aes(x = State, y = avg_rating, fill = State,
label = paste("(", round(avg_rating, 2), ",", provider_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 2, vjust = -1) +
theme_classic() +
xlab("Hospitals by State") +
ylab("Average rating") +
ggtitle("Average Ratings by State")
graph_by_prov_state
avg_by_hosp_ownership <- hospital_ratings %>%
group_by(Hospital.Ownership) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_hosp_ownership
## # A tibble: 10 x 3
## Hospital.Ownership avg_rating hospital_count
## <chr> <dbl> <int>
## 1 Physician 4.10 31
## 2 Voluntary non-profit - Church 3.15 275
## 3 Voluntary non-profit - Other 3.11 348
## 4 Voluntary non-profit - Private 3.07 1284
## 5 Government - Hospital District or Authority 2.96 261
## 6 Government - Federal 2.93 15
## 7 Proprietary 2.91 633
## 8 Government - Local 2.79 168
## 9 Government - State 2.64 44
## 10 Tribal 2.5 2
# A tibble: 10 x 3
# Hospital.Ownership avg_rating hospital_count
# <chr> <dbl> <int>
# 1 Physician 4.10 31
# 2 Voluntary non-profit - Church 3.15 275
# 3 Voluntary non-profit - Other 3.11 348
# 4 Voluntary non-profit - Private 3.07 1284
# 5 Government - Hospital District or Authority 2.96 261
# 6 Government - Federal 2.93 15
# 7 Proprietary 2.91 633
# 8 Government - Local 2.79 168
# 9 Government - State 2.64 44
# 10 Tribal 2.5 2
graph_by_hosp_owner <- avg_by_hosp_ownership %>%
ggplot(aes(x = Hospital.Ownership, y = avg_rating, fill = Hospital.Ownership,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 2, vjust = -1) +
theme_classic() +
xlab("Hospitals by Onwership") +
ylab("Average rating") +
ggtitle("Average Ratings by Onwership")
graph_by_hosp_owner
# we see good average ratings for "Physician" owned hospitals and voluntary non-profit owned hospitals
# Comparitively low ratings for tribal, Government -local owned hospitals.
library(cowplot)
##
## ********************************************************
## Note: As of version 1.0.0, cowplot does not change the
## default ggplot2 theme anymore. To recover the previous
## behavior, execute:
## theme_set(theme_cowplot())
## ********************************************************
##
## Attaching package: 'cowplot'
## The following object is masked from 'package:ggthemes':
##
## theme_map
grid_plot <- plot_grid(graph_by_prov_state, graph_by_hosp_owner)
grid_plot
# Average of Readmission Ratings
avg_by_readmission <- hospital_ratings %>%
group_by(Readmission.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_readmission
## # A tibble: 4 x 3
## Readmission.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 <NA> 3.92 124
## 2 Above the National average 3.57 791
## 3 Same as the National average 3.03 1299
## 4 Below the National average 2.38 847
# A tibble: 4 x 3
# Readmission.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 NA 3.92 124
# 2 Above the National average 3.57 791
# 3 Same as the National average 3.03 1299
# 4 Below the National average 2.38 847
# Here we see that the average ratings of hospitals which have above the national average for readmissions is about 3.57 and around 811 hospitals rank here
# A considerable number of hospitals have average same as national average and their average rating is around 3.12
# Those which are below the national average have an average rating around 2.38.
# Above and Below national average hospitals are similar in number with respect to the readmissions count.
graph_by_readmission <- avg_by_readmission %>%
ggplot(aes(x = Readmission.national.comparison, y = avg_rating, fill = Readmission.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Readmission Comparison ") +
ylab("Average rating") +
ggtitle("Average Ratings by Readmissions")
graph_by_readmission
readmission_gp <- round(avg_by_readmission$avg_rating[2] - avg_by_readmission$avg_rating[4], 2)
# Average Mortality ratings
avg_by_mortality <- hospital_ratings %>%
group_by(Mortality.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_mortality
## # A tibble: 4 x 3
## Mortality.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 <NA> 3.63 194
## 2 Above the National average 3.29 400
## 3 Same as the National average 3.01 2124
## 4 Below the National average 2.48 343
# A tibble: 4 x 3
# Mortality.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 NA 3.63 194
# 2 Above the National average 3.29 400
# 3 Same as the National average 3.01 2124
# 4 Below the National average 2.48 343
# Excluding the NA's, around 400 hospitals have mortality rate above the national average
# A considerable number of hospitals (2124) have mortality rate same as the National average
# Few hospitals (343) have the mortality rate below the National average.
graph_by_mortality <- avg_by_mortality %>%
ggplot(aes(x = Mortality.national.comparison, y = avg_rating, fill = Mortality.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Morality Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Mortality")
graph_by_mortality
mortality_gp <- round(avg_by_mortality$avg_rating[2] - avg_by_mortality$avg_rating[4], 2)
# Average safety ratings
avg_by_safety <- hospital_ratings %>%
group_by(Safety.of.care.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_safety
## # A tibble: 4 x 3
## Safety.of.care.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 Above the National average 3.44 804
## 2 <NA> 3.12 205
## 3 Same as the National average 3.09 1379
## 4 Below the National average 2.36 673
# # A tibble: 4 x 3
# Safety.of.care.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 Above the National average 3.44 804
# 2 NA 3.12 205
# 3 Same as the National average 3.09 1379
# 4 Below the National average 2.36 673
# Here we see that the average ratings of hospitals which have above the national average for safety of care is about 3.44 and around 804 hospitals rank here
# A considerable number of hospitals have average same as national average and their average rating is around 3.09
# Those which are below the national average have an average rating around 2.36.
graph_by_safety <- avg_by_safety %>%
ggplot(aes(x = Safety.of.care.national.comparison, y = avg_rating, fill = Safety.of.care.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Safety of care Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Safety")
graph_by_safety
safety_gp <- round(avg_by_safety$avg_rating[1] - avg_by_safety$avg_rating[4], 2)
# Average experience ratings
avg_by_experience <- hospital_ratings %>%
group_by(Patient.experience.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_experience
## # A tibble: 4 x 3
## Patient.experience.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 Above the National average 3.69 887
## 2 Same as the National average 3.11 996
## 3 <NA> 2.86 99
## 4 Below the National average 2.42 1079
# # A tibble: 4 x 3
# Patient.experience.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 Above the National average 3.69 887
# 2 Same as the National average 3.11 996
# 3 NA 2.86 99
# 4 Below the National average 2.42 1079
graph_by_experience <- avg_by_experience %>%
ggplot(aes(x = Patient.experience.national.comparison, y = avg_rating, fill = Patient.experience.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Patient Experience Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Experience")
graph_by_experience
experience_gp <- round(avg_by_experience$avg_rating[1] - avg_by_experience$avg_rating[4], 2)
# Average medical ratings
avg_by_medical <- hospital_ratings %>%
group_by(Efficient.use.of.medical.imaging.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_medical
## # A tibble: 4 x 3
## Efficient.use.of.medical.imaging.national.comp… avg_rating hospital_count
## <chr> <dbl> <int>
## 1 <NA> 3.09 556
## 2 Same as the National average 3.04 1806
## 3 Above the National average 3.03 359
## 4 Below the National average 2.86 340
# # A tibble: 4 x 3
# Efficient.use.of.medical.imaging.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 NA 3.09 556
# 2 Same as the National average 3.04 1806
# 3 Above the National average 3.03 359
# 4 Below the National average 2.86 340
# there's not much difference between the average ratings of above and same as
# national average hospitals by medical group variables
graph_by_medical <- avg_by_medical %>%
ggplot(aes(x = Efficient.use.of.medical.imaging.national.comparison, y = avg_rating, fill = Efficient.use.of.medical.imaging.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 2, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Use of Medical Imaging Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Medical")
graph_by_medical
medical_gp <- round(avg_by_medical$avg_rating[3] - avg_by_medical$avg_rating[4], 2)
# Average timeliness ratings
avg_by_timeliness <- hospital_ratings %>%
group_by(Timeliness.of.care.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_timeliness
## # A tibble: 4 x 3
## Timeliness.of.care.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 <NA> 3.62 151
## 2 Above the National average 3.22 826
## 3 Same as the National average 3.12 1185
## 4 Below the National average 2.62 899
# # A tibble: 4 x 3
# Timeliness.of.care.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 NA 3.62 151
# 2 Above the National average 3.22 826
# 3 Same as the National average 3.12 1185
# 4 Below the National average 2.62 899
# timeliness has a significant effect on the average rating (from 2.62 to 3.22)
graph_by_timeliness <- avg_by_timeliness %>%
ggplot(aes(x = Timeliness.of.care.national.comparison, y = avg_rating, fill = Timeliness.of.care.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Timeliness of care Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Timeliness")
graph_by_timeliness
timeliness_gp <- round(avg_by_timeliness$avg_rating[2] - avg_by_timeliness$avg_rating[4], 2)
# Average effectiveness ratings
avg_by_effectiveness <- hospital_ratings %>%
group_by(Effectiveness.of.care.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_effectiveness
## # A tibble: 3 x 3
## Effectiveness.of.care.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 Above the National average 3.08 991
## 2 Same as the National average 3.07 1665
## 3 Below the National average 2.72 405
# # A tibble: 4 x 3
# Effectiveness.of.care.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 Above the National average 3.08 991
# 2 Same as the National average 3.07 1665
# 3 Below the National average 2.72 405
graph_by_effectiveness <- avg_by_effectiveness %>%
ggplot(aes(x = Effectiveness.of.care.national.comparison, y = avg_rating, fill = Effectiveness.of.care.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Effectiveness of care Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Effectiveness")
graph_by_effectiveness
effectiveness_gp <- round(avg_by_effectiveness$avg_rating[1] - avg_by_effectiveness$avg_rating[3], 2)
# Average Star ratings by each group and its impact over the ratings
group_impact <- data.frame(readmission = readmission_gp, mortality = mortality_gp, safety = safety_gp, experience = experience_gp,
medical = medical_gp, timeliness = timeliness_gp, effectiveness = effectiveness_gp)
group_impacts_in_order <- t(group_impact[order(group_impact, decreasing = T)])
group_impacts_in_order
## [,1]
## experience 1.27
## readmission 1.19
## safety 1.08
## mortality 0.81
## timeliness 0.60
## effectiveness 0.36
## medical 0.17
# [,1]
# experience 1.27
# readmission 1.19
# safety 1.08
# mortality 0.81
# timeliness 0.60
# effectiveness 0.36
# medical 0.17
# As per the cms measures the top 4 experience, readmission, safety and mortality have a 22% of weightage
# and the last 3 groups timeliness, effectiveness, medical have 4% of weightage
grid_plot1 <- plot_grid(graph_by_readmission, graph_by_mortality, graph_by_safety, graph_by_experience, graph_by_medical, graph_by_timeliness, graph_by_effectiveness)
grid_plot1
Bivariate analysis
# correlation of readmission
readmission <- read_master
str(readmission)
## 'data.frame': 4818 obs. of 16 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ READM_30_AMI_score : num 0.409 0.2 0.826 NA NA ...
## $ READM_30_CABG_score : num -0.615 NA -0.704 NA NA ...
## $ READM_30_COPD_score : num -0.8668 1.5743 0.1569 0.0782 0.6294 ...
## $ READM_30_HF_score : num 0.3704 0.0364 0.9047 0.5707 -0.765 ...
## $ READM_30_HIP_KNEE_score : num -0.884 -1.968 -0.703 NA NA ...
## $ READM_30_HOSP_WIDE_score: num 0.215 0.821 0.215 -1.237 -0.148 ...
## $ READM_30_PN_score : num -1.105 0.496 -0.549 -0.131 0.774 ...
## $ READM_30_STK_score : num -0.125 -0.777 0.528 -0.125 NA ...
correlation_readmission <- round(cor(readmission[, -c(1:8)], use = "pairwise.complete.obs"), 2)
col1 <- colorRampPalette(c("#7F0000", "red", "#FF7F00", "yellow", "white", "cyan", "#007FFF", "blue", "#00007F"))
col2 <- colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FFFFFF", "#D1E5F0", "#92C5DE", "#4393C3", "#2166AC", "#053061"))
col3 <- colorRampPalette(c("red", "white", "blue"))
col4 <- colorRampPalette(c("#7F0000", "red", "#FF7F00", "yellow", "#7FFF7F", "cyan", "#007FFF", "blue", "#00007F"))
corrplot(correlation_readmission, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon")
write.csv(correlation_readmission, "correlation_readmission.csv")
# Visual Observations:
# The correlation measures for READM_30_HIP_KNEE_score is very low accross all the variables.
# The correlation measures for READM_30_CABG_score is very low accross all the variables.
# READM_30_HOSP_WIDE_score has very good correlation measures accross all the variables.
# Now let us check, how the measures have an impact on the ratings.
readmission <- readmission[, -c(2:8)]
readmission_rating <- merge(readmission, master_data_y, by = "Provider.ID")
readmission_group_summary <- readmission_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.
readmission_group_summary
## # A tibble: 6 x 9
## Hospital.overal… READM_30_AMI_sc… READM_30_CABG_s… READM_30_COPD_s…
## <int> <dbl> <dbl> <dbl>
## 1 1 -0.588 -0.579 -1.01
## 2 2 -0.413 -0.214 -0.392
## 3 3 0.0501 -0.0370 0.0135
## 4 4 0.362 0.313 0.346
## 5 5 1.01 0.454 0.965
## 6 NA -0.698 0.228 -0.0357
## # … with 5 more variables: READM_30_HF_score <dbl>,
## # READM_30_HIP_KNEE_score <dbl>, READM_30_HOSP_WIDE_score <dbl>,
## # READM_30_PN_score <dbl>, READM_30_STK_score <dbl>
# # A tibble: 6 x 9
# Hospital.overall.rating READM_30_AMI_score READM_30_CABG_score READM_30_COPD_score READM_30_HF_score READM_30_HIP_KNEE_score READM_30_HOSP_WIDE_score READM_30_PN_score READM_30_STK_score
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -0.588 -0.579 -1.01 -1.14 -0.309 -1.75 -1.24 -0.946
# 2 -0.413 -0.214 -0.392 -0.480 -0.283 -0.787 -0.609 -0.323
# 3 0.0501 -0.0370 0.0135 -0.00873 -0.0543 -0.0208 0.0322 0.0372
# 4 0.362 0.313 0.346 0.436 0.202 0.612 0.409 0.327
# 5 1.01 0.454 0.965 1.26 0.883 1.66 0.850 0.663
# NA -0.698 0.228 -0.0357 -0.0303 -0.00712 0.00494 0.123 0.155
# We notice that, the ratings increase as all scores of the measures for readmission increase.
# Hence, all the readmission measures have an important role in predicting the ratings.
# correlation of mortality
mortality <- mort_master
str(mortality)
## 'data.frame': 4818 obs. of 15 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.1 NA NA ...
## $ MORT_30_CABG_score : num -0.996 NA -0.881 NA NA ...
## $ MORT_30_COPD_score : num -1.094 0.434 0.883 -1.094 -0.105 ...
## $ MORT_30_HF_score : num -0.166 -2.286 -2.355 -1.534 -0.371 ...
## $ MORT_30_PN_score : num 0.428 -2.103 -0.862 -1.148 0.332 ...
## $ MORT_30_STK_score : num -0.282 -0.342 -1.784 -1.003 NA ...
## $ PSI_4_SURG_COMP_score: num -1.71 -2.3 -3.33 NA NA ...
correlation_mortality <- round(cor(mortality[, -c(1:8)], use = "pairwise.complete.obs"), 2)
corrplot(correlation_mortality, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon")
write.csv(correlation_mortality, "correlation_mortality.csv")
# The correlation values are too low compared to the correlation values for readmission.
# With low correlation values, we see HF, PN and STK scores have some significance.
# Probably these measures would have an impact in the ratings.
# Now let us check, how the measures have an impact on the ratings.
mortality <- mortality[, -c(2:8)]
mortality_rating <- merge(mortality, master_data_y, by = "Provider.ID")
mortality_group_summary <- mortality_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
mortality_group_summary
## # A tibble: 6 x 8
## Hospital.overal… MORT_30_AMI_sco… MORT_30_CABG_sc… MORT_30_COPD_sc…
## <int> <dbl> <dbl> <dbl>
## 1 1 -0.436 -0.226 -0.411
## 2 2 -0.230 -0.118 -0.199
## 3 3 0.00607 -0.0151 -0.0153
## 4 4 0.235 0.133 0.180
## 5 5 0.809 0.394 0.597
## 6 NA -0.234 -0.136 0.0583
## # … with 4 more variables: MORT_30_HF_score <dbl>, MORT_30_PN_score <dbl>,
## # MORT_30_STK_score <dbl>, PSI_4_SURG_COMP_score <dbl>
# # A tibble: 6 x 8
# Hospital.overall.rating MORT_30_AMI_score MORT_30_CABG_score MORT_30_COPD_score MORT_30_HF_score MORT_30_PN_score MORT_30_STK_score PSI_4_SURG_COMP_score
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -0.436 -0.226 -0.411 0.0892 -0.503 -0.554 -0.601
# 2 -0.230 -0.118 -0.199 -0.104 -0.339 -0.0885 -0.240
# 3 0.00607 -0.0151 -0.0153 -0.0783 -0.0513 -0.0146 0.0177
# 4 0.235 0.133 0.180 0.210 0.376 0.168 0.290
# 5 0.809 0.394 0.597 0.753 0.899 0.395 0.443
# NA -0.234 -0.136 0.0583 -0.251 -0.0476 -0.132 1.40
# We Can see that the rating increases as each measure score increases
# but for HF score, it behaves differently.
# AMI, HF, ON have an effect on the ratings.
# correlation of safety
safety <- safe_master
str(safety)
## 'data.frame': 4818 obs. of 16 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ COMP_HIP_KNEE_score: num -1.355 0.0745 -1.355 NA NA ...
## $ HAI_1_SIR_score : num -2.351 -1.023 0.389 NA NA ...
## $ HAI_2_SIR_score : num -2.088 0.05 -0.357 1.054 NA ...
## $ HAI_3_SIR_score : num -1.135 0.723 0.818 NA NA ...
## $ HAI_4_SIR_score : num 1.02 NA NA NA NA ...
## $ HAI_5_SIR_score : num 0.647 -0.457 -0.312 NA NA ...
## $ HAI_6_SIR_score : num 0.0566 0.7984 0.5887 1.5849 0.4489 ...
## $ PSI_90_SAFETY_score: num 1.2108 0.2304 -0.1156 0.5764 -0.0579 ...
correlation_safety <- round(cor(safety[, -c(1:8)], use = "pairwise.complete.obs"), 2)
corrplot(correlation_safety, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon")
write.csv(correlation_safety, "correlation_safety.csv")
# COMP_HIP_KNEE_score has a slight negative correlation between HAI_1,2,3 and slight +ve correlation for HAI_4,5,6 and PSI.
# HAI-1, 2 and 3 are correlated compared to the other measures, all are not correlated with HAI-5, HAI-6, PSI and COMP-HIP-KNEE score
# HAI-1 has correlation with HAI-5.
# Now let us check, how the measures have an impact on the ratings.
safety <- safety[, -(2:8)]
safety_rating <- merge(safety, master_data_y, by = "Provider.ID")
safety_group_summary <- safety_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
safety_group_summary
## # A tibble: 6 x 9
## Hospital.overal… COMP_HIP_KNEE_s… HAI_1_SIR_score HAI_2_SIR_score
## <int> <dbl> <dbl> <dbl>
## 1 1 -0.204 -0.445 -0.526
## 2 2 -0.149 -0.172 -0.109
## 3 3 -0.0765 0.0489 0.0654
## 4 4 0.125 0.177 0.0745
## 5 5 0.846 0.232 0.120
## 6 NA 0.153 -0.0970 -0.0262
## # … with 5 more variables: HAI_3_SIR_score <dbl>, HAI_4_SIR_score <dbl>,
## # HAI_5_SIR_score <dbl>, HAI_6_SIR_score <dbl>,
## # PSI_90_SAFETY_score <dbl>
# # A tibble: 6 x 9
# Hospital.overall.rating COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score PSI_90_SAFETY_score
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -0.204 -0.445 -0.526 -0.173 -0.395 -0.341 -0.0257 -1.88
# 2 -0.149 -0.172 -0.109 -0.0459 0.0362 -0.0710 -0.100 -0.531
# 3 -0.0765 0.0489 0.0654 0.0119 0.0434 0.00232 -0.0240 0.0652
# 4 0.125 0.177 0.0745 0.0613 -0.0488 0.152 0.00476 0.471
# 5 0.846 0.232 0.120 -0.0285 0.166 0.280 0.510 0.942
# NA 0.153 -0.0970 -0.0262 0.0660 0.0439 -0.0714 0.403 0.0689
# As per the above summary, it looks like COMP_HIP_KNEE_score, PSI_90_SAFETY_score have a significant impact on the ratings.
# correlation of experience
experience <- expe_master
correlation_experience <- round(cor(experience[, -c(1:8)], use = "pairwise.complete.obs"), 2)
par(pin = c(5, 5)) ## (width, height) in inches
par(omi = c(0, 0.5, 0.5, 0.5)) ## (bottom, left, top, right) in inches
corrplot(correlation_experience, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon", tl.cex = 0.7, tl.offset = 0.5, number.cex = 0.8, tl.pos = 't')
write.csv(correlation_experience, "correlation_experience.csv")
# we can visualize that all the variables are positively correlated with each other.
# This implies that the overall hospital experience, which is all about the hospitality and clear communication, helps improve the ratings.
# All the measures play a vital role in the ratings.
# Now let us check, how the measures have an impact on the ratings.
experience <- experience[, -c(2:8)]
experience_rating <- merge(experience, master_data_y, by = "Provider.ID")
experience_group_summary <- experience_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
experience_group_summary
## # A tibble: 6 x 12
## Hospital.overal… H_CLEAN_LINEAR_… H_COMP_1_LINEAR… H_COMP_2_LINEAR…
## <int> <dbl> <dbl> <dbl>
## 1 1 -1.28 -1.57 -1.17
## 2 2 -0.705 -0.790 -0.709
## 3 3 -0.0223 -0.0303 -0.00778
## 4 4 0.530 0.602 0.504
## 5 5 0.922 1.14 0.822
## 6 NA 0.378 0.473 0.410
## # … with 8 more variables: H_COMP_3_LINEAR_SCORE <dbl>,
## # H_COMP_4_LINEAR_SCORE <dbl>, H_COMP_5_LINEAR_SCORE <dbl>,
## # H_COMP_6_LINEAR_SCORE <dbl>, H_COMP_7_LINEAR_SCORE <dbl>,
## # H_HSP_RATING_LINEAR_SCORE <dbl>, H_QUIET_LINEAR_SCORE <dbl>,
## # H_RECMND_LINEAR_SCORE <dbl>
# # A tibble: 6 x 12
# Hospital.overall.rating H_CLEAN_LINEAR_~ H_COMP_1_LINEAR~ H_COMP_2_LINEAR~ H_COMP_3_LINEAR~ H_COMP_4_LINEAR~ H_COMP_5_LINEAR~ H_COMP_6_LINEAR~ H_COMP_7_LINEAR~ H_HSP_RATING_LI~ H_QUIET_LINEAR_~ H_RECMND_LINEAR~
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -1.28 -1.57 -1.17 -1.64 -1.48 -1.31 -1.32 -1.37 -1.53 -1.09 -1.31
# 2 -0.705 -0.790 -0.709 -0.806 -0.767 -0.739 -0.661 -0.784 -0.790 -0.578 -0.737
# 3 -0.0223 -0.0303 -0.00778 -0.00465 -0.0252 -0.0574 -0.0351 -0.0777 -0.0649 -0.0224 -0.0874
# 4 0.530 0.602 0.504 0.566 0.577 0.577 0.524 0.620 0.617 0.364 0.600
# 5 0.922 1.14 0.822 1.06 1.05 1.02 0.883 1.25 1.38 0.958 1.37
# NA 0.378 0.473 0.410 0.613 0.449 0.570 0.425 0.605 0.554 0.729 0.500
# This implies all the measures in experience are highly important measures.
# Correlation of medical
medical <- medi_master
correlation_medical <- round(cor(medical[, -c(1:8)], use = "pairwise.complete.obs"), 2)
corrplot(correlation_medical, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon")
write.csv(correlation_medical, "correlation_medical.csv")
# Only OP-10 and OP_11 have some good correlation.
# Rest all the correlations are negligible.
# Now let us check, how the measures have an impact on the ratings.
medical <- medical[, -c(2:8)]
medical_rating <- merge(medical, master_data_y, by = "Provider.ID")
medical_group_summary <- medical_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
medical_group_summary
## # A tibble: 6 x 6
## Hospital.overal… OP_10_score OP_11_score OP_13_score OP_14_score
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.0750 0.132 0.0318 -0.685
## 2 2 -0.0888 -0.0955 -0.0360 -0.273
## 3 3 -0.0114 -0.0134 0.0318 0.0235
## 4 4 0.133 0.110 -0.0156 0.100
## 5 5 0.131 0.240 -0.135 0.317
## 6 NA -0.213 -0.272 0.00673 1.06
## # … with 1 more variable: OP_8_score <dbl>
# # A tibble: 6 x 6
# Hospital.overall.rating OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
# <int> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 0.0750 0.132 0.0318 -0.685 0.130
# 2 -0.0888 -0.0955 -0.0360 -0.273 0.0312
# 3 -0.0114 -0.0134 0.0318 0.0235 -0.0176
# 4 0.133 0.110 -0.0156 0.100 0.00368
# 5 0.131 0.240 -0.135 0.317 0.0970
# NA -0.213 -0.272 0.00673 1.06 -0.612
# Only OP_14 seems significant, but the overall weight of medical group itself is quite less
# Correlation of timeliness
timeliness <- time_master
correlation_timeliness <- round(cor(timeliness[, -c(1:8)], use = "pairwise.complete.obs"), 2)
corrplot(correlation_timeliness, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon")
write.csv(correlation_timeliness, "correlation_timeliness.csv")
# All ED scores have good correlation with OP_18b, OP_20, and OP_21.
# Even OP_18b, 20 and 21 have good correlations with each other.
# Now let us check, how the measures have an impact on the ratings.
timeliness <- timeliness[, -c(2:8)]
timeliness_rating <- merge(timeliness, master_data_y, by = "Provider.ID")
timeliness_group_summary <- timeliness_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
timeliness_group_summary
## # A tibble: 6 x 8
## Hospital.overal… ED_1b_score ED_2b_score OP_18b_score OP_20_score
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 -1.40 -1.30 -0.982 -1.01
## 2 2 -0.514 -0.508 -0.432 -0.266
## 3 3 0.101 0.0991 0.0551 0.0390
## 4 4 0.242 0.206 0.0933 0.152
## 5 5 0.301 0.174 -0.0454 0.125
## 6 NA 0.393 0.499 0.747 0.288
## # … with 3 more variables: OP_21_score <dbl>, OP_3b_score <dbl>,
## # OP_5_score <dbl>
# # A tibble: 6 x 8
# Hospital.overall.rating ED_1b_score ED_2b_score OP_18b_score OP_20_score OP_21_score OP_3b_score OP_5_score
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -1.40 -1.30 -0.982 -1.01 -0.763 -0.403 -0.841
# 2 -0.514 -0.508 -0.432 -0.266 -0.294 -0.137 -0.0914
# 3 0.101 0.0991 0.0551 0.0390 0.0297 -0.0210 -0.0155
# 4 0.242 0.206 0.0933 0.152 0.236 0.182 0.204
# 5 0.301 0.174 -0.0454 0.125 0.263 0.542 0.341
# NA 0.393 0.499 0.747 0.288 0.160 -0.355 -0.181
# The above implies ED_1b and OP_21, OP_3b, OP_5 are the most important measures
# Since the overall weightage of the group is only 4%, the impact might be less.
# Correlation of effectiveness
effectiveness <- effe_master
correlation_effectiveness <- round(cor(effectiveness[, -c(1:8)], use = "pairwise.complete.obs"), 2)
par(pin = c(75, 75)) ## (width, height) in inches
par(omi = c(0, 0.8, 0.8, 0.8)) ## (bottom, left, top, right) in inches
corrplot(correlation_effectiveness, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon", tl.cex = 0.8, tl.offset = 0.7, number.cex = 0.7,
tl.pos = 'b')
write.csv(correlation_effectiveness, "correlation_effectiveness.csv")
# We can see very good correlations for STK_4, STK5,STK_6, STK_8, VTE_1, VTE_2 and VTE_6.
# CAC_3 and IMM_2, STK_6, STK_8, VTE_1 are positively correlated.
# Now let us check, how the measures have an impact on the ratings.
effectiveness <- effectiveness[, -c(2:8)]
effectiveness_rating <- merge(effectiveness, master_data_y, by = "Provider.ID")
effectiveness_group_summary <- effectiveness_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
effectiveness_group_summary
## # A tibble: 6 x 19
## Hospital.overal… CAC_3_score IMM_2_score IMM_3_OP_27_FAC… OP_22_score
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.0933 -0.237 -0.286 -0.669
## 2 2 -0.0145 0.0486 -0.217 -0.237
## 3 3 0.215 0.0779 0.00515 0.000349
## 4 4 -0.0927 0.229 0.205 0.207
## 5 5 0.397 0.311 0.227 0.401
## 6 NA -0.254 -0.980 -0.0751 0.268
## # … with 14 more variables: OP_23_score <dbl>, OP_29_score <dbl>,
## # OP_30_score <dbl>, OP_4_score <dbl>, PC_01_score <dbl>,
## # STK_4_score <dbl>, STK_5_score <dbl>, STK_6_score <dbl>,
## # STK_8_score <dbl>, VTE_1_score <dbl>, VTE_2_score <dbl>,
## # VTE_3_score <dbl>, VTE_5_score <dbl>, VTE_6_score <dbl>
write.csv(effectiveness_group_summary, "effectiveness_rating_summary.csv")
# We see a mixed behaviour here, ratings increase as measures increase for few and viceversa.
# This implies that VTE (1, 2, 3, 5) measures are correlated to each other, they might play role in the ratings.
# Similar observations are there for STK measures
EDA Completed
Random Forest Modelling
library(caTools)
library(rpart)
library(randomForest)
## randomForest 4.6-14
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
## The following object is masked from 'package:gridExtra':
##
## combine
## The following object is masked from 'package:ggplot2':
##
## margin
## The following object is masked from 'package:dplyr':
##
## combine
library(rpart.plot)
library(RColorBrewer)
library(rattle)
## Rattle: A free graphical interface for data science with R.
## Version 5.3.0 Copyright (c) 2006-2018 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.
##
## Attaching package: 'rattle'
## The following object is masked from 'package:randomForest':
##
## importance
library(caret)
## Loading required package: lattice
# We will use the cleaned master data as data set-
# cleaned_master_data
### split the train and test set
set.seed(123)
data = sample.split(cleaned_master_data, SplitRatio = 0.3)
train = subset(cleaned_master_data, data == FALSE)
test = subset(cleaned_master_data, data == TRUE)
dim(train)
## [1] 2570 54
# [1] 2570 54
dim(test)
## [1] 1078 54
# [1] 1078 54
str(train)
## 'data.frame': 2570 obs. of 54 variables:
## $ Provider.ID : int 10005 10006 10007 10008 10012 10016 10019 10021 10022 10023 ...
## $ READM_30_AMI_score : num 0.1999 0.8261 0.0956 0.0956 0.8261 ...
## $ READM_30_COPD_score : num 1.5743 0.1569 0.0782 0.6294 0.6294 ...
## $ READM_30_HF_score : num 0.0364 0.9047 0.5707 -0.765 -0.9653 ...
## $ READM_30_HIP_KNEE_score : num -1.9681 -0.7033 0.0194 0.0194 0.0194 ...
## $ READM_30_HOSP_WIDE_score : num 0.821 0.215 -1.237 -0.148 0.336 ...
## $ READM_30_PN_score : num 0.496 -0.549 -0.131 0.774 -1.593 ...
## $ READM_30_STK_score : num -0.777 0.5279 -0.1245 0.0619 -0.2177 ...
## $ MORT_30_AMI_score : num -1.5457 -2.1047 0.0514 0.0514 -2.1846 ...
## $ MORT_30_COPD_score : num 0.434 0.883 -1.094 -0.105 -1.004 ...
## $ MORT_30_HF_score : num -2.286 -2.355 -1.534 -0.371 -0.234 ...
## $ MORT_30_PN_score : num -2.103 -0.862 -1.148 0.332 -3.772 ...
## $ MORT_30_STK_score : num -0.3421 -1.7844 -1.0031 0.0786 -0.5224 ...
## $ COMP_HIP_KNEE_score : num 0.0745 -1.355 0.0745 0.0745 0.4318 ...
## $ HAI_1_SIR_score : num -1.023 0.389 0.149 0.149 0.149 ...
## $ HAI_2_SIR_score : num 0.05 -0.357 1.054 0.127 1.054 ...
## $ HAI_3_SIR_score : num 0.723 0.818 0.16 0.16 0.16 ...
## $ HAI_6_SIR_score : num 0.798 0.589 1.585 0.449 1.148 ...
## $ PSI_90_SAFETY_score : num 0.2304 -0.1156 0.5764 -0.0579 -0.0579 ...
## $ H_CLEAN_LINEAR_SCORE : num -1.1116 -1.1116 0.4419 -0.0759 -0.3349 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.129 -0.129 -0.129 0.265 0.265 ...
## $ H_COMP_2_LINEAR_SCORE : num 0.8596 0.8596 1.678 0.0412 0.8596 ...
## $ H_COMP_3_LINEAR_SCORE : num -0.29 -0.5175 0.3923 -0.0626 -0.0626 ...
## $ H_COMP_4_LINEAR_SCORE : num 0.167 -0.22 0.553 0.167 0.167 ...
## $ H_COMP_5_LINEAR_SCORE : num 0.2845 -0.182 0.751 0.0513 -0.4152 ...
## $ H_COMP_6_LINEAR_SCORE : num 0.3087 -1.0964 -0.2534 0.0276 0.3087 ...
## $ H_COMP_7_LINEAR_SCORE : num -0.183 -0.532 0.165 0.165 -0.88 ...
## $ H_HSP_RATING_LINEAR_SCORE : num 0.3915 -1.1451 -0.5304 0.0842 0.0842 ...
## $ H_QUIET_LINEAR_SCORE : num 0.57722 0.57722 1.75119 -0.00976 0.57722 ...
## $ H_RECMND_LINEAR_SCORE : num 0.2213 -0.9226 -0.465 -0.0075 -0.6938 ...
## $ OP_10_score : num -0.423 -0.277 -1.498 0.524 0.28 ...
## $ OP_11_score : num -1.204 -0.245 -0.502 0.389 -1.306 ...
## $ OP_13_score : num -0.3052 2.3296 0.0428 0.0428 0.7885 ...
## $ OP_14_score : num -0.649 -0.97 0.1 1.171 0.1 ...
## $ ED_1b_score : num 0.342 0.594 0.574 0.953 0.865 ...
## $ ED_2b_score : num 0.463 0.357 0.508 0.69 0.751 ...
## $ OP_18b_score : num 0.616 0.235 0.568 1.068 0.544 ...
## $ OP_20_score : num -0.049 1.009 -0.733 -0.049 -0.485 ...
## $ OP_21_score : num -0.38 -0.267 -2.078 0.243 -0.323 ...
## $ OP_5_score : num -0.714 0.248 0.248 0.248 1.595 ...
## $ IMM_2_score : num 0.53 0.613 0.53 0.197 0.613 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.099 0.169 -2.11 -2.781 0.37 ...
## $ OP_22_score : num -0.108 0.438 0.438 0.438 -0.108 ...
## $ OP_29_score : num 0.692 -0.102 -2.627 0.836 0.981 ...
## $ OP_30_score : num 0.5 0.329 -3.448 0.715 0.844 ...
## $ OP_4_score : num 0.546 0.368 -1.232 0.368 0.724 ...
## $ PC_01_score : num 0.324 0.539 0.324 0.324 -2.254 ...
## $ STK_6_score : num -0.9154 0.1984 -3.0191 0.3222 -0.0491 ...
## $ STK_8_score : num 0.618 -0.296 0.344 0.344 0.618 ...
## $ VTE_1_score : num 0.178 0.412 0.334 0.489 0.489 ...
## $ VTE_2_score : num -0.45 -1.282 0.381 0.381 0.215 ...
## $ VTE_3_score : num 0.819 -0.887 0.332 0.332 0.21 ...
## $ VTE_5_score : num 0.6352 -0.0759 0.3685 0.3685 0.6352 ...
## $ Hospital.overall.rating : Factor w/ 5 levels "1","2","3","4",..: 3 2 3 3 3 3 2 4 3 3 ...
# We will use mtry=20, we will use ntree = 500,1000 and 1500 as per the mentor's suggestions.
model_rf1 <- randomForest(Hospital.overall.rating ~ ., data = train, promiximity = FALSE, ntree = 500, mtry = 20, do.trace = TRUE, na.action = na.omit)
## ntree OOB 1 2 3 4 5
## 1: 36.66% 60.00% 37.87% 29.20% 42.63% 67.74%
## 2: 36.65% 53.49% 41.90% 29.93% 40.90% 65.12%
## 3: 37.28% 52.46% 41.97% 30.13% 42.20% 69.81%
## 4: 37.34% 44.12% 43.95% 31.50% 40.25% 60.00%
## 5: 37.06% 44.59% 42.92% 30.63% 41.23% 62.50%
## 6: 35.09% 44.87% 42.95% 28.43% 38.83% 52.24%
## 7: 35.09% 45.12% 43.45% 26.82% 41.11% 56.72%
## 8: 34.13% 48.19% 40.34% 26.68% 39.82% 52.86%
## 9: 33.04% 53.01% 40.38% 25.04% 37.86% 56.34%
## 10: 31.47% 44.05% 39.32% 24.14% 35.93% 52.11%
## 11: 31.51% 44.71% 38.30% 23.07% 37.91% 58.90%
## 12: 31.04% 51.76% 37.71% 23.17% 35.62% 57.53%
## 13: 29.46% 47.06% 37.92% 21.08% 33.98% 57.53%
## 14: 29.22% 48.24% 36.65% 21.08% 34.13% 54.79%
## 15: 28.93% 48.24% 34.75% 20.65% 34.58% 60.27%
## 16: 27.89% 49.41% 36.02% 19.43% 32.19% 57.53%
## 17: 27.95% 48.24% 37.92% 19.02% 32.34% 54.05%
## 18: 27.00% 50.59% 36.44% 17.98% 31.59% 52.70%
## 19: 27.39% 55.29% 35.59% 18.53% 32.19% 51.35%
## 20: 26.81% 52.94% 36.65% 17.51% 31.15% 54.05%
## 21: 25.80% 55.29% 34.53% 16.64% 30.40% 51.35%
## 22: 25.68% 54.12% 35.59% 16.01% 30.55% 51.35%
## 23: 25.41% 51.76% 36.23% 16.17% 29.06% 51.35%
## 24: 25.45% 54.12% 36.02% 16.09% 29.36% 50.00%
## 25: 24.59% 52.94% 33.90% 15.54% 28.61% 51.35%
## 26: 25.10% 56.47% 35.38% 15.93% 28.32% 51.35%
## 27: 24.98% 52.94% 35.17% 15.54% 29.36% 50.00%
## 28: 25.25% 54.12% 35.38% 16.09% 28.91% 51.35%
## 29: 25.10% 55.29% 36.02% 15.85% 28.46% 48.65%
## 30: 24.32% 55.29% 34.96% 14.98% 28.02% 47.30%
## 31: 24.24% 54.12% 33.69% 14.91% 28.76% 48.65%
## 32: 24.28% 54.12% 34.11% 14.75% 28.46% 52.70%
## 33: 23.89% 54.12% 33.26% 14.43% 28.17% 52.70%
## 34: 23.93% 54.12% 34.32% 13.80% 28.76% 52.70%
## 35: 24.01% 55.29% 34.96% 13.72% 28.61% 52.70%
## 36: 24.20% 55.29% 34.53% 14.35% 28.46% 52.70%
## 37: 23.54% 54.12% 33.90% 13.25% 28.61% 52.70%
## 38: 23.74% 50.59% 35.17% 13.56% 28.32% 52.70%
## 39: 23.70% 52.94% 34.11% 13.56% 28.61% 52.70%
## 40: 23.35% 49.41% 34.53% 13.01% 28.46% 52.70%
## 41: 23.07% 52.94% 33.69% 12.78% 28.02% 52.70%
## 42: 23.39% 52.94% 33.05% 13.25% 28.76% 52.70%
## 43: 23.77% 57.65% 33.47% 13.49% 28.76% 54.05%
## 44: 23.39% 54.12% 33.47% 12.93% 28.61% 55.41%
## 45: 23.42% 56.47% 33.90% 12.54% 28.91% 55.41%
## 46: 23.74% 56.47% 35.59% 12.93% 28.17% 55.41%
## 47: 23.07% 57.65% 33.47% 12.30% 28.46% 52.70%
## 48: 23.11% 56.47% 33.90% 12.38% 28.32% 52.70%
## 49: 23.39% 60.00% 34.32% 12.46% 28.46% 52.70%
## 50: 23.00% 57.65% 35.38% 11.51% 28.32% 52.70%
## 51: 22.61% 60.00% 34.11% 11.59% 27.57% 50.00%
## 52: 22.53% 60.00% 33.26% 11.44% 28.17% 50.00%
## 53: 22.92% 56.47% 33.47% 12.30% 28.17% 51.35%
## 54: 22.57% 58.82% 33.90% 11.44% 28.02% 50.00%
## 55: 22.45% 57.65% 33.69% 11.59% 27.42% 51.35%
## 56: 22.37% 58.82% 32.42% 11.51% 28.17% 50.00%
## 57: 22.37% 60.00% 32.84% 11.44% 27.87% 50.00%
## 58: 22.37% 60.00% 32.84% 11.12% 28.32% 51.35%
## 59: 22.37% 60.00% 33.05% 11.12% 28.17% 51.35%
## 60: 22.65% 58.82% 33.05% 11.51% 28.61% 51.35%
## 61: 21.95% 58.82% 31.36% 10.88% 28.32% 51.35%
## 62: 22.37% 61.18% 32.42% 11.04% 28.76% 50.00%
## 63: 22.33% 61.18% 32.42% 10.73% 29.21% 50.00%
## 64: 22.57% 61.18% 32.84% 10.88% 29.21% 52.70%
## 65: 22.22% 58.82% 33.26% 10.41% 28.91% 51.35%
## 66: 22.22% 60.00% 33.05% 10.25% 29.06% 52.70%
## 67: 22.45% 60.00% 33.90% 10.65% 28.61% 52.70%
## 68: 22.22% 61.18% 33.69% 10.02% 28.91% 52.70%
## 69: 22.10% 60.00% 33.47% 10.25% 28.46% 51.35%
## 70: 21.98% 58.82% 33.47% 10.17% 28.17% 52.70%
## 71: 22.06% 61.18% 33.69% 10.25% 28.02% 51.35%
## 72: 22.26% 61.18% 33.26% 10.33% 28.91% 51.35%
## 73: 22.41% 63.53% 34.32% 10.25% 28.46% 52.70%
## 74: 22.14% 63.53% 33.69% 10.09% 28.32% 51.35%
## 75: 22.30% 64.71% 32.84% 10.41% 28.76% 51.35%
## 76: 22.02% 62.35% 32.84% 10.17% 28.46% 51.35%
## 77: 21.87% 62.35% 32.42% 9.70% 28.91% 52.70%
## 78: 21.60% 61.18% 33.47% 9.62% 27.27% 54.05%
## 79: 21.56% 61.18% 32.84% 9.31% 28.17% 54.05%
## 80: 21.56% 61.18% 32.42% 9.46% 28.17% 54.05%
## 81: 21.40% 61.18% 32.20% 9.54% 27.57% 54.05%
## 82: 21.67% 62.35% 33.05% 9.78% 27.57% 52.70%
## 83: 21.98% 62.35% 33.47% 9.86% 28.32% 52.70%
## 84: 21.83% 62.35% 33.69% 10.02% 27.27% 52.70%
## 85: 21.48% 61.18% 32.84% 9.86% 27.12% 51.35%
## 86: 21.63% 64.71% 32.63% 9.54% 27.87% 52.70%
## 87: 21.91% 63.53% 33.90% 10.02% 27.27% 52.70%
## 88: 21.52% 63.53% 32.20% 9.70% 27.57% 52.70%
## 89: 21.75% 62.35% 34.32% 9.62% 27.27% 52.70%
## 90: 21.44% 61.18% 33.90% 9.23% 27.27% 52.70%
## 91: 21.44% 62.35% 33.47% 9.31% 27.42% 51.35%
## 92: 21.25% 62.35% 31.57% 9.46% 27.57% 52.70%
## 93: 21.40% 61.18% 32.42% 9.31% 27.87% 54.05%
## 94: 21.05% 62.35% 31.57% 9.23% 27.12% 54.05%
## 95: 21.32% 62.35% 32.20% 9.46% 27.27% 54.05%
## 96: 21.13% 62.35% 31.78% 9.31% 27.27% 52.70%
## 97: 20.97% 60.00% 31.78% 8.83% 27.72% 54.05%
## 98: 21.05% 60.00% 31.57% 9.07% 28.02% 51.35%
## 99: 21.05% 60.00% 31.99% 8.83% 27.87% 54.05%
## 100: 21.25% 62.35% 32.20% 8.99% 28.17% 51.35%
## 101: 21.13% 62.35% 32.20% 8.83% 28.02% 51.35%
## 102: 21.05% 62.35% 32.20% 8.68% 27.87% 52.70%
## 103: 20.93% 60.00% 31.78% 8.75% 27.87% 52.70%
## 104: 20.86% 62.35% 32.20% 8.20% 28.02% 52.70%
## 105: 20.89% 61.18% 32.42% 8.36% 27.87% 52.70%
## 106: 21.32% 61.18% 32.84% 8.99% 28.02% 52.70%
## 107: 21.17% 61.18% 32.42% 8.68% 28.32% 52.70%
## 108: 21.36% 61.18% 33.26% 8.83% 28.17% 52.70%
## 109: 21.44% 60.00% 32.63% 8.91% 28.76% 54.05%
## 110: 21.71% 62.35% 33.26% 9.15% 28.61% 54.05%
## 111: 21.28% 62.35% 32.63% 8.68% 28.32% 54.05%
## 112: 21.44% 62.35% 33.05% 8.68% 28.61% 54.05%
## 113: 21.28% 62.35% 32.84% 8.68% 28.17% 54.05%
## 114: 21.09% 61.18% 32.84% 8.52% 27.87% 54.05%
## 115: 21.48% 61.18% 33.47% 8.60% 28.76% 54.05%
## 116: 20.97% 60.00% 32.84% 8.44% 27.87% 52.70%
## 117: 21.44% 62.35% 33.47% 8.60% 28.61% 52.70%
## 118: 21.28% 63.53% 33.05% 8.36% 28.61% 52.70%
## 119: 21.17% 61.18% 33.47% 8.28% 28.32% 52.70%
## 120: 21.32% 63.53% 33.69% 8.28% 28.46% 52.70%
## 121: 21.13% 63.53% 33.26% 8.36% 27.72% 54.05%
## 122: 21.13% 62.35% 33.26% 8.28% 28.02% 54.05%
## 123: 20.97% 62.35% 31.99% 8.52% 27.87% 54.05%
## 124: 21.28% 62.35% 33.05% 8.60% 28.17% 54.05%
## 125: 21.28% 63.53% 33.47% 8.60% 27.72% 54.05%
## 126: 21.13% 62.35% 32.63% 8.28% 28.46% 54.05%
## 127: 21.32% 64.71% 32.63% 8.60% 28.32% 54.05%
## 128: 21.40% 63.53% 33.05% 8.68% 28.17% 55.41%
## 129: 21.36% 64.71% 33.47% 8.44% 28.17% 54.05%
## 130: 21.36% 64.71% 33.26% 8.52% 28.02% 55.41%
## 131: 21.25% 63.53% 33.47% 8.60% 27.57% 54.05%
## 132: 21.21% 63.53% 33.90% 8.36% 27.57% 54.05%
## 133: 21.05% 63.53% 33.26% 8.44% 27.27% 54.05%
## 134: 21.13% 64.71% 33.69% 8.44% 27.12% 54.05%
## 135: 21.13% 63.53% 33.47% 8.20% 27.87% 54.05%
## 136: 21.05% 63.53% 32.42% 8.44% 27.72% 55.41%
## 137: 20.97% 64.71% 32.84% 8.28% 27.42% 54.05%
## 138: 20.97% 64.71% 32.42% 8.44% 27.42% 54.05%
## 139: 21.28% 64.71% 32.63% 8.83% 27.72% 54.05%
## 140: 21.21% 64.71% 32.42% 8.68% 27.87% 54.05%
## 141: 21.13% 64.71% 31.99% 8.60% 28.02% 54.05%
## 142: 21.13% 65.88% 31.99% 8.60% 27.87% 54.05%
## 143: 21.05% 64.71% 31.99% 8.75% 27.42% 54.05%
## 144: 21.01% 64.71% 31.99% 8.68% 27.42% 54.05%
## 145: 20.89% 64.71% 31.99% 8.60% 27.12% 54.05%
## 146: 20.97% 64.71% 31.99% 8.44% 27.72% 54.05%
## 147: 20.86% 64.71% 31.78% 8.44% 27.42% 54.05%
## 148: 20.78% 63.53% 31.36% 8.60% 27.42% 52.70%
## 149: 20.93% 63.53% 31.99% 8.44% 27.72% 54.05%
## 150: 21.01% 63.53% 31.99% 8.60% 27.72% 54.05%
## 151: 20.86% 62.35% 31.78% 8.44% 27.72% 54.05%
## 152: 20.97% 63.53% 31.99% 8.60% 27.57% 54.05%
## 153: 20.93% 62.35% 31.99% 8.91% 26.83% 55.41%
## 154: 20.86% 63.53% 31.99% 8.60% 26.97% 55.41%
## 155: 20.93% 63.53% 31.78% 8.75% 27.27% 54.05%
## 156: 20.89% 63.53% 31.78% 8.75% 27.12% 54.05%
## 157: 21.01% 63.53% 31.99% 8.99% 26.97% 54.05%
## 158: 21.21% 63.53% 32.84% 8.75% 27.42% 55.41%
## 159: 20.93% 63.53% 31.78% 8.68% 27.27% 55.41%
## 160: 21.09% 63.53% 31.78% 8.91% 27.42% 55.41%
## 161: 20.74% 61.18% 31.36% 8.68% 27.12% 55.41%
## 162: 21.05% 63.53% 31.57% 8.68% 27.87% 55.41%
## 163: 20.97% 62.35% 31.78% 8.36% 28.17% 55.41%
## 164: 20.66% 61.18% 30.93% 8.36% 27.72% 55.41%
## 165: 20.70% 63.53% 30.72% 8.44% 27.57% 55.41%
## 166: 20.93% 64.71% 31.14% 8.36% 28.17% 55.41%
## 167: 20.66% 64.71% 30.72% 8.20% 27.72% 55.41%
## 168: 20.35% 64.71% 30.51% 7.81% 27.42% 55.41%
## 169: 20.62% 64.71% 30.72% 8.36% 27.27% 55.41%
## 170: 20.58% 63.53% 30.72% 8.36% 27.27% 55.41%
## 171: 20.70% 63.53% 31.36% 8.20% 27.57% 55.41%
## 172: 20.31% 64.71% 31.14% 7.89% 26.68% 55.41%
## 173: 20.74% 63.53% 31.36% 8.36% 27.42% 55.41%
## 174: 20.47% 62.35% 31.14% 7.97% 27.42% 55.41%
## 175: 20.54% 61.18% 31.36% 8.20% 27.27% 55.41%
## 176: 20.47% 61.18% 30.93% 8.12% 27.42% 55.41%
## 177: 20.43% 62.35% 30.93% 8.04% 27.27% 55.41%
## 178: 20.54% 62.35% 30.93% 8.12% 27.57% 55.41%
## 179: 20.31% 62.35% 30.93% 7.97% 26.97% 55.41%
## 180: 20.08% 61.18% 30.72% 7.65% 26.97% 55.41%
## 181: 20.31% 62.35% 31.14% 7.89% 26.97% 55.41%
## 182: 20.51% 62.35% 31.36% 7.97% 27.42% 55.41%
## 183: 20.39% 62.35% 31.36% 7.73% 27.42% 55.41%
## 184: 20.47% 63.53% 30.51% 8.04% 27.57% 55.41%
## 185: 20.35% 61.18% 30.72% 8.04% 27.27% 55.41%
## 186: 20.43% 61.18% 30.93% 8.04% 27.42% 55.41%
## 187: 20.47% 61.18% 30.93% 8.12% 27.42% 55.41%
## 188: 20.19% 61.18% 30.72% 7.81% 27.12% 55.41%
## 189: 20.23% 60.00% 30.72% 7.89% 27.27% 55.41%
## 190: 20.16% 60.00% 30.93% 7.89% 26.68% 56.76%
## 191: 20.08% 60.00% 31.14% 7.73% 26.53% 56.76%
## 192: 20.23% 60.00% 30.93% 8.04% 26.68% 56.76%
## 193: 20.31% 60.00% 31.36% 8.12% 26.53% 56.76%
## 194: 20.35% 60.00% 30.93% 8.36% 26.53% 56.76%
## 195: 20.19% 60.00% 31.14% 7.97% 26.53% 56.76%
## 196: 20.27% 61.18% 31.14% 8.12% 26.38% 56.76%
## 197: 20.31% 61.18% 30.93% 8.12% 26.68% 56.76%
## 198: 20.43% 61.18% 31.14% 8.12% 26.97% 56.76%
## 199: 20.47% 60.00% 31.57% 8.12% 26.97% 56.76%
## 200: 20.47% 61.18% 31.78% 8.20% 26.68% 55.41%
## 201: 20.39% 60.00% 31.36% 8.28% 26.68% 55.41%
## 202: 20.39% 60.00% 30.93% 8.12% 27.12% 56.76%
## 203: 20.35% 60.00% 31.14% 8.04% 26.97% 56.76%
## 204: 20.43% 61.18% 31.14% 8.20% 26.83% 56.76%
## 205: 20.35% 61.18% 31.14% 8.12% 26.68% 56.76%
## 206: 20.51% 61.18% 31.57% 8.20% 26.97% 55.41%
## 207: 20.43% 61.18% 31.36% 8.20% 26.83% 55.41%
## 208: 20.35% 61.18% 30.93% 8.12% 26.83% 56.76%
## 209: 20.47% 60.00% 31.57% 8.28% 26.83% 55.41%
## 210: 20.27% 60.00% 31.78% 7.97% 26.53% 55.41%
## 211: 20.51% 60.00% 32.20% 8.12% 26.83% 55.41%
## 212: 20.47% 60.00% 31.57% 8.20% 26.97% 55.41%
## 213: 20.74% 61.18% 32.20% 8.44% 26.97% 55.41%
## 214: 20.43% 61.18% 31.14% 8.44% 26.53% 55.41%
## 215: 20.43% 61.18% 31.36% 8.28% 26.68% 55.41%
## 216: 20.43% 60.00% 30.93% 8.36% 26.97% 55.41%
## 217: 20.31% 60.00% 31.57% 8.20% 26.38% 55.41%
## 218: 20.19% 60.00% 31.14% 8.36% 25.93% 55.41%
## 219: 20.47% 60.00% 31.78% 8.44% 26.38% 55.41%
## 220: 20.39% 60.00% 31.57% 8.12% 26.83% 55.41%
## 221: 20.47% 60.00% 31.99% 8.28% 26.53% 55.41%
## 222: 20.51% 61.18% 31.57% 8.28% 26.83% 55.41%
## 223: 20.35% 60.00% 31.57% 8.12% 26.68% 55.41%
## 224: 20.31% 61.18% 31.57% 8.04% 26.53% 55.41%
## 225: 20.54% 60.00% 32.20% 8.12% 26.97% 55.41%
## 226: 20.74% 61.18% 32.63% 8.20% 27.12% 55.41%
## 227: 20.62% 61.18% 31.99% 8.12% 27.27% 55.41%
## 228: 20.54% 61.18% 31.57% 8.04% 27.42% 55.41%
## 229: 20.86% 61.18% 31.99% 8.60% 27.27% 55.41%
## 230: 20.51% 61.18% 31.57% 8.12% 27.12% 55.41%
## 231: 20.74% 61.18% 31.78% 8.36% 27.42% 55.41%
## 232: 20.74% 61.18% 31.99% 8.20% 27.57% 55.41%
## 233: 20.66% 61.18% 32.20% 8.36% 26.83% 55.41%
## 234: 20.47% 61.18% 31.36% 8.36% 26.68% 55.41%
## 235: 20.58% 61.18% 31.78% 8.44% 26.68% 55.41%
## 236: 20.62% 61.18% 32.20% 8.44% 26.53% 55.41%
## 237: 20.47% 61.18% 31.36% 8.52% 26.38% 55.41%
## 238: 20.58% 61.18% 31.78% 8.36% 26.83% 55.41%
## 239: 20.74% 61.18% 31.99% 8.75% 26.53% 55.41%
## 240: 20.54% 61.18% 31.78% 8.60% 26.23% 55.41%
## 241: 20.62% 61.18% 31.78% 8.68% 26.38% 55.41%
## 242: 20.66% 61.18% 31.78% 8.60% 26.68% 55.41%
## 243: 20.54% 61.18% 31.99% 8.44% 26.38% 55.41%
## 244: 20.31% 61.18% 31.78% 8.04% 26.38% 55.41%
## 245: 20.62% 61.18% 32.20% 8.52% 26.38% 55.41%
## 246: 20.78% 61.18% 32.20% 8.52% 26.97% 55.41%
## 247: 20.51% 61.18% 31.99% 8.20% 26.68% 55.41%
## 248: 20.70% 61.18% 32.42% 8.28% 26.83% 56.76%
## 249: 20.51% 61.18% 31.99% 8.28% 26.53% 55.41%
## 250: 20.58% 61.18% 32.20% 8.20% 26.68% 56.76%
## 251: 20.62% 61.18% 31.99% 8.28% 26.83% 56.76%
## 252: 20.51% 61.18% 31.78% 8.20% 26.83% 55.41%
## 253: 20.58% 61.18% 31.57% 8.36% 27.12% 54.05%
## 254: 20.82% 61.18% 32.42% 8.52% 26.97% 55.41%
## 255: 20.66% 61.18% 31.78% 8.44% 26.97% 55.41%
## 256: 20.62% 60.00% 31.78% 8.60% 26.83% 54.05%
## 257: 20.70% 60.00% 31.78% 8.52% 27.12% 55.41%
## 258: 20.66% 61.18% 31.78% 8.52% 26.83% 55.41%
## 259: 20.35% 60.00% 31.36% 8.52% 26.23% 54.05%
## 260: 20.54% 60.00% 32.20% 8.44% 26.38% 55.41%
## 261: 20.43% 60.00% 31.78% 8.36% 26.53% 54.05%
## 262: 20.54% 60.00% 31.36% 8.75% 26.38% 55.41%
## 263: 20.39% 60.00% 31.57% 8.44% 26.23% 55.41%
## 264: 20.39% 60.00% 31.57% 8.44% 26.23% 55.41%
## 265: 20.39% 60.00% 32.20% 8.20% 26.38% 54.05%
## 266: 20.35% 60.00% 31.99% 8.36% 26.08% 54.05%
## 267: 20.35% 60.00% 31.78% 8.36% 26.08% 55.41%
## 268: 20.43% 60.00% 31.99% 8.44% 26.08% 55.41%
## 269: 20.23% 60.00% 31.57% 8.36% 25.93% 54.05%
## 270: 20.12% 60.00% 31.78% 8.04% 25.93% 54.05%
## 271: 20.04% 60.00% 31.36% 8.12% 25.78% 54.05%
## 272: 20.08% 60.00% 30.93% 8.28% 25.93% 54.05%
## 273: 20.12% 60.00% 31.36% 8.28% 25.78% 54.05%
## 274: 20.00% 60.00% 31.14% 8.20% 25.63% 54.05%
## 275: 20.19% 60.00% 31.36% 8.28% 26.08% 54.05%
## 276: 20.08% 60.00% 31.57% 7.97% 26.08% 54.05%
## 277: 20.27% 60.00% 31.99% 8.28% 25.78% 55.41%
## 278: 20.12% 60.00% 31.57% 8.12% 25.78% 55.41%
## 279: 20.08% 60.00% 31.57% 7.89% 26.08% 55.41%
## 280: 20.04% 60.00% 31.57% 7.81% 26.08% 55.41%
## 281: 20.12% 60.00% 31.78% 7.89% 26.08% 55.41%
## 282: 19.81% 60.00% 31.36% 7.65% 25.78% 54.05%
## 283: 20.19% 60.00% 32.42% 7.81% 26.23% 54.05%
## 284: 19.96% 60.00% 31.57% 7.73% 26.08% 54.05%
## 285: 20.31% 60.00% 32.20% 7.97% 26.53% 54.05%
## 286: 20.27% 60.00% 32.63% 7.65% 26.68% 54.05%
## 287: 20.16% 60.00% 31.78% 7.73% 26.68% 54.05%
## 288: 20.23% 60.00% 32.20% 7.81% 26.68% 52.70%
## 289: 20.23% 60.00% 32.42% 7.89% 26.23% 54.05%
## 290: 20.19% 61.18% 32.42% 7.73% 26.23% 54.05%
## 291: 20.23% 61.18% 32.20% 7.73% 26.68% 52.70%
## 292: 20.19% 61.18% 32.20% 7.73% 26.53% 52.70%
## 293: 20.31% 61.18% 32.63% 7.73% 26.68% 52.70%
## 294: 20.12% 61.18% 32.42% 7.57% 26.38% 52.70%
## 295: 20.19% 61.18% 31.99% 7.73% 26.53% 54.05%
## 296: 20.23% 61.18% 32.20% 7.65% 26.68% 54.05%
## 297: 20.12% 61.18% 31.78% 7.81% 26.38% 52.70%
## 298: 20.27% 61.18% 31.99% 7.97% 26.53% 52.70%
## 299: 20.23% 61.18% 31.78% 7.73% 26.83% 54.05%
## 300: 20.19% 61.18% 31.78% 7.89% 26.53% 52.70%
## 301: 20.27% 61.18% 32.42% 7.81% 26.53% 52.70%
## 302: 20.16% 61.18% 31.99% 7.81% 26.38% 52.70%
## 303: 20.31% 61.18% 31.99% 8.04% 26.53% 52.70%
## 304: 20.27% 61.18% 31.78% 8.04% 26.53% 52.70%
## 305: 20.35% 61.18% 32.42% 7.89% 26.68% 52.70%
## 306: 20.35% 61.18% 32.20% 8.04% 26.53% 52.70%
## 307: 20.23% 61.18% 32.20% 7.97% 26.23% 52.70%
## 308: 20.19% 61.18% 31.99% 7.89% 26.23% 54.05%
## 309: 20.12% 61.18% 31.78% 7.81% 26.23% 54.05%
## 310: 20.23% 61.18% 32.20% 7.81% 26.38% 54.05%
## 311: 20.12% 61.18% 31.99% 7.73% 26.23% 54.05%
## 312: 20.12% 61.18% 32.20% 7.73% 26.08% 54.05%
## 313: 20.27% 61.18% 32.20% 7.97% 26.23% 54.05%
## 314: 20.39% 61.18% 32.63% 7.81% 26.53% 55.41%
## 315: 20.35% 61.18% 32.20% 7.89% 26.53% 55.41%
## 316: 20.31% 61.18% 32.20% 7.89% 26.38% 55.41%
## 317: 20.23% 61.18% 31.99% 7.89% 26.23% 55.41%
## 318: 20.35% 61.18% 31.99% 8.04% 26.38% 55.41%
## 319: 20.27% 61.18% 31.99% 7.97% 26.38% 54.05%
## 320: 20.12% 61.18% 31.57% 7.81% 26.23% 55.41%
## 321: 20.27% 61.18% 31.57% 7.97% 26.53% 55.41%
## 322: 20.16% 61.18% 31.99% 7.81% 26.23% 54.05%
## 323: 20.19% 61.18% 31.57% 7.89% 26.53% 54.05%
## 324: 20.19% 61.18% 32.20% 7.81% 26.23% 54.05%
## 325: 20.23% 61.18% 32.20% 7.89% 26.23% 54.05%
## 326: 20.19% 61.18% 32.63% 7.73% 26.08% 54.05%
## 327: 20.23% 61.18% 32.42% 7.89% 26.08% 54.05%
## 328: 20.16% 61.18% 32.20% 7.81% 25.93% 55.41%
## 329: 20.08% 61.18% 32.20% 7.81% 25.78% 54.05%
## 330: 19.92% 61.18% 31.78% 7.73% 25.63% 54.05%
## 331: 20.00% 61.18% 32.20% 7.73% 25.78% 52.70%
## 332: 20.00% 61.18% 32.42% 7.81% 25.63% 51.35%
## 333: 19.92% 61.18% 32.42% 7.57% 25.78% 51.35%
## 334: 19.84% 61.18% 32.42% 7.57% 25.48% 51.35%
## 335: 19.88% 61.18% 32.20% 7.73% 25.48% 51.35%
## 336: 20.00% 61.18% 32.63% 7.81% 25.48% 51.35%
## 337: 19.88% 61.18% 32.63% 7.65% 25.34% 51.35%
## 338: 20.04% 61.18% 32.63% 7.73% 25.48% 54.05%
## 339: 19.81% 61.18% 32.42% 7.41% 25.48% 52.70%
## 340: 20.00% 61.18% 32.84% 7.57% 25.48% 54.05%
## 341: 19.96% 61.18% 32.42% 7.65% 25.48% 54.05%
## 342: 20.00% 61.18% 32.63% 7.81% 25.19% 54.05%
## 343: 20.19% 61.18% 32.84% 7.81% 25.78% 54.05%
## 344: 19.92% 61.18% 32.63% 7.65% 25.19% 54.05%
## 345: 19.96% 61.18% 32.63% 7.49% 25.48% 55.41%
## 346: 19.84% 61.18% 32.42% 7.49% 25.19% 55.41%
## 347: 20.16% 61.18% 32.20% 7.81% 25.93% 55.41%
## 348: 19.77% 61.18% 31.99% 7.49% 25.19% 55.41%
## 349: 19.77% 61.18% 31.99% 7.49% 25.19% 55.41%
## 350: 19.88% 61.18% 32.20% 7.65% 25.19% 55.41%
## 351: 19.77% 61.18% 31.78% 7.49% 25.48% 54.05%
## 352: 19.84% 61.18% 31.99% 7.41% 25.78% 54.05%
## 353: 19.92% 61.18% 31.99% 7.65% 25.63% 54.05%
## 354: 19.84% 61.18% 31.78% 7.41% 25.93% 54.05%
## 355: 19.84% 61.18% 31.57% 7.41% 26.08% 54.05%
## 356: 20.00% 61.18% 32.20% 7.49% 26.08% 54.05%
## 357: 20.00% 61.18% 32.42% 7.49% 25.93% 54.05%
## 358: 19.96% 61.18% 31.99% 7.49% 26.08% 54.05%
## 359: 20.00% 61.18% 32.20% 7.49% 26.08% 54.05%
## 360: 20.04% 61.18% 32.63% 7.33% 26.23% 54.05%
## 361: 20.00% 61.18% 31.99% 7.41% 26.38% 54.05%
## 362: 19.88% 61.18% 31.78% 7.41% 26.08% 54.05%
## 363: 20.04% 61.18% 32.20% 7.49% 26.23% 54.05%
## 364: 20.04% 61.18% 31.99% 7.57% 26.08% 55.41%
## 365: 20.00% 61.18% 32.42% 7.57% 25.63% 55.41%
## 366: 19.73% 61.18% 31.57% 7.57% 25.34% 54.05%
## 367: 19.92% 61.18% 31.78% 7.65% 25.63% 55.41%
## 368: 20.00% 61.18% 31.78% 7.65% 25.93% 55.41%
## 369: 20.08% 61.18% 31.99% 7.65% 26.08% 55.41%
## 370: 19.96% 61.18% 31.78% 7.73% 25.63% 55.41%
## 371: 19.96% 61.18% 31.57% 7.73% 25.78% 55.41%
## 372: 19.84% 61.18% 31.57% 7.57% 25.63% 55.41%
## 373: 19.92% 61.18% 31.36% 7.57% 26.08% 55.41%
## 374: 19.96% 61.18% 30.93% 7.57% 26.53% 55.41%
## 375: 19.88% 61.18% 30.93% 7.57% 26.23% 55.41%
## 376: 19.84% 61.18% 30.72% 7.73% 25.93% 55.41%
## 377: 19.92% 61.18% 30.93% 7.57% 26.38% 55.41%
## 378: 19.81% 61.18% 30.72% 7.73% 25.78% 55.41%
## 379: 19.73% 61.18% 30.93% 7.73% 25.34% 55.41%
## 380: 19.88% 61.18% 30.72% 7.89% 25.78% 55.41%
## 381: 19.88% 61.18% 31.14% 7.89% 25.48% 55.41%
## 382: 19.81% 61.18% 31.14% 7.81% 25.34% 55.41%
## 383: 19.81% 61.18% 30.93% 7.97% 25.19% 55.41%
## 384: 19.92% 61.18% 30.72% 8.04% 25.63% 55.41%
## 385: 20.12% 61.18% 31.78% 8.04% 25.63% 55.41%
## 386: 19.77% 61.18% 30.72% 7.89% 25.34% 55.41%
## 387: 19.92% 61.18% 31.36% 8.04% 25.19% 55.41%
## 388: 19.84% 61.18% 30.93% 7.97% 25.34% 55.41%
## 389: 19.96% 61.18% 31.14% 8.04% 25.48% 55.41%
## 390: 19.96% 61.18% 31.36% 7.89% 25.63% 55.41%
## 391: 20.00% 61.18% 31.36% 7.97% 25.63% 55.41%
## 392: 19.96% 61.18% 31.14% 8.04% 25.48% 55.41%
## 393: 20.04% 61.18% 31.14% 8.04% 25.78% 55.41%
## 394: 20.04% 61.18% 31.36% 7.89% 25.93% 55.41%
## 395: 19.96% 61.18% 31.36% 7.97% 25.48% 55.41%
## 396: 20.12% 61.18% 31.57% 7.97% 25.93% 55.41%
## 397: 19.96% 61.18% 31.36% 7.89% 25.63% 55.41%
## 398: 19.92% 61.18% 31.14% 7.97% 25.48% 55.41%
## 399: 19.96% 61.18% 30.93% 7.97% 25.78% 55.41%
## 400: 20.08% 62.35% 31.36% 7.97% 25.78% 55.41%
## 401: 19.92% 61.18% 31.57% 7.89% 25.34% 55.41%
## 402: 19.84% 61.18% 31.57% 7.89% 25.04% 55.41%
## 403: 19.96% 61.18% 31.78% 7.89% 25.34% 55.41%
## 404: 19.96% 61.18% 31.99% 7.89% 25.19% 55.41%
## 405: 19.92% 61.18% 31.78% 7.81% 25.34% 55.41%
## 406: 19.88% 61.18% 31.78% 7.73% 25.34% 55.41%
## 407: 20.00% 61.18% 31.99% 7.81% 25.48% 55.41%
## 408: 20.00% 61.18% 32.42% 7.81% 25.19% 55.41%
## 409: 20.00% 61.18% 31.99% 7.89% 25.34% 55.41%
## 410: 19.92% 61.18% 31.78% 7.89% 25.19% 55.41%
## 411: 19.92% 61.18% 32.42% 7.73% 25.04% 55.41%
## 412: 19.92% 61.18% 32.20% 7.89% 24.89% 55.41%
## 413: 19.92% 61.18% 31.99% 7.89% 25.04% 55.41%
## 414: 20.00% 61.18% 32.20% 7.89% 25.19% 55.41%
## 415: 20.00% 61.18% 31.99% 7.89% 25.34% 55.41%
## 416: 20.00% 61.18% 31.99% 7.81% 25.48% 55.41%
## 417: 20.08% 61.18% 32.42% 7.81% 25.48% 55.41%
## 418: 20.00% 61.18% 31.99% 7.89% 25.34% 55.41%
## 419: 20.04% 61.18% 31.99% 7.89% 25.48% 55.41%
## 420: 20.04% 61.18% 32.42% 7.81% 25.34% 55.41%
## 421: 20.00% 61.18% 31.99% 7.81% 25.48% 55.41%
## 422: 20.00% 61.18% 31.99% 7.81% 25.48% 55.41%
## 423: 19.96% 61.18% 31.36% 7.89% 25.63% 55.41%
## 424: 20.12% 61.18% 31.57% 8.04% 25.78% 55.41%
## 425: 20.16% 62.35% 32.20% 7.89% 25.63% 55.41%
## 426: 20.19% 62.35% 32.20% 7.89% 25.78% 55.41%
## 427: 20.16% 61.18% 31.99% 7.97% 25.78% 55.41%
## 428: 20.12% 61.18% 31.99% 7.89% 25.78% 55.41%
## 429: 20.08% 62.35% 31.99% 7.81% 25.63% 55.41%
## 430: 20.08% 62.35% 31.57% 7.89% 25.78% 55.41%
## 431: 19.88% 62.35% 31.14% 7.57% 25.93% 55.41%
## 432: 19.96% 62.35% 31.14% 7.81% 25.78% 55.41%
## 433: 20.00% 62.35% 31.57% 7.81% 25.63% 55.41%
## 434: 19.96% 61.18% 31.14% 7.81% 25.93% 55.41%
## 435: 19.92% 61.18% 31.36% 7.73% 25.78% 55.41%
## 436: 19.96% 62.35% 31.36% 7.81% 25.63% 55.41%
## 437: 19.92% 62.35% 31.36% 7.81% 25.48% 55.41%
## 438: 20.00% 62.35% 31.57% 7.89% 25.48% 55.41%
## 439: 19.92% 61.18% 31.57% 7.89% 25.34% 55.41%
## 440: 19.88% 61.18% 31.14% 7.89% 25.48% 55.41%
## 441: 19.92% 61.18% 31.36% 7.81% 25.63% 55.41%
## 442: 19.81% 62.35% 31.14% 7.81% 25.34% 54.05%
## 443: 19.84% 61.18% 31.78% 7.73% 25.34% 54.05%
## 444: 19.81% 61.18% 31.36% 7.81% 25.34% 54.05%
## 445: 19.84% 61.18% 31.36% 7.73% 25.63% 54.05%
## 446: 19.81% 61.18% 31.36% 7.65% 25.63% 54.05%
## 447: 19.84% 61.18% 31.36% 7.73% 25.63% 54.05%
## 448: 19.92% 61.18% 31.57% 7.81% 25.63% 54.05%
## 449: 19.92% 61.18% 31.57% 7.81% 25.63% 54.05%
## 450: 19.84% 61.18% 31.14% 7.81% 25.63% 54.05%
## 451: 19.81% 61.18% 31.14% 7.73% 25.63% 54.05%
## 452: 19.77% 60.00% 30.93% 7.81% 25.63% 54.05%
## 453: 19.81% 60.00% 30.93% 7.81% 25.78% 54.05%
## 454: 19.81% 60.00% 30.93% 7.81% 25.78% 54.05%
## 455: 20.00% 60.00% 31.57% 7.97% 25.78% 54.05%
## 456: 20.08% 60.00% 31.78% 8.04% 25.78% 54.05%
## 457: 19.96% 60.00% 31.57% 7.89% 25.78% 54.05%
## 458: 20.08% 60.00% 31.36% 8.12% 25.93% 54.05%
## 459: 20.00% 60.00% 31.57% 7.89% 25.93% 54.05%
## 460: 19.65% 60.00% 30.93% 7.57% 25.63% 54.05%
## 461: 19.84% 60.00% 31.14% 7.97% 25.48% 54.05%
## 462: 19.81% 60.00% 31.36% 7.73% 25.63% 54.05%
## 463: 19.88% 60.00% 31.36% 7.89% 25.63% 54.05%
## 464: 19.92% 60.00% 31.36% 7.81% 25.93% 54.05%
## 465: 19.84% 60.00% 31.36% 7.73% 25.78% 54.05%
## 466: 19.77% 60.00% 31.36% 7.65% 25.78% 52.70%
## 467: 19.81% 61.18% 31.57% 7.57% 25.78% 52.70%
## 468: 19.81% 61.18% 31.36% 7.57% 25.78% 54.05%
## 469: 19.84% 61.18% 31.57% 7.65% 25.78% 52.70%
## 470: 19.92% 61.18% 31.78% 7.65% 25.93% 52.70%
## 471: 19.96% 61.18% 31.99% 7.73% 25.78% 52.70%
## 472: 19.96% 61.18% 32.20% 7.65% 25.78% 52.70%
## 473: 19.96% 61.18% 32.42% 7.49% 25.93% 52.70%
## 474: 19.88% 61.18% 32.20% 7.49% 25.78% 52.70%
## 475: 19.77% 61.18% 32.20% 7.57% 25.19% 52.70%
## 476: 19.96% 61.18% 31.99% 7.65% 25.93% 52.70%
## 477: 19.77% 61.18% 31.57% 7.57% 25.63% 52.70%
## 478: 19.84% 61.18% 31.99% 7.73% 25.34% 52.70%
## 479: 19.92% 61.18% 31.78% 7.81% 25.63% 52.70%
## 480: 19.84% 61.18% 31.57% 7.73% 25.63% 52.70%
## 481: 19.96% 62.35% 31.78% 7.73% 25.78% 52.70%
## 482: 19.88% 62.35% 31.36% 7.65% 25.93% 52.70%
## 483: 19.81% 61.18% 31.57% 7.65% 25.63% 52.70%
## 484: 19.84% 61.18% 31.57% 7.65% 25.78% 52.70%
## 485: 19.65% 61.18% 31.36% 7.49% 25.48% 52.70%
## 486: 19.73% 61.18% 31.36% 7.65% 25.48% 52.70%
## 487: 19.73% 61.18% 31.36% 7.57% 25.63% 52.70%
## 488: 19.81% 61.18% 31.36% 7.73% 25.63% 52.70%
## 489: 19.73% 61.18% 31.36% 7.65% 25.48% 52.70%
## 490: 19.69% 61.18% 31.36% 7.65% 25.34% 52.70%
## 491: 19.81% 61.18% 31.36% 7.73% 25.63% 52.70%
## 492: 19.69% 61.18% 31.36% 7.49% 25.63% 52.70%
## 493: 19.69% 61.18% 31.14% 7.57% 25.63% 52.70%
## 494: 19.69% 61.18% 31.36% 7.57% 25.48% 52.70%
## 495: 19.69% 61.18% 31.14% 7.57% 25.63% 52.70%
## 496: 19.69% 61.18% 31.14% 7.49% 25.78% 52.70%
## 497: 19.73% 61.18% 31.14% 7.65% 25.63% 52.70%
## 498: 19.73% 61.18% 31.36% 7.57% 25.63% 52.70%
## 499: 19.61% 61.18% 31.36% 7.49% 25.34% 52.70%
## 500: 19.65% 61.18% 31.57% 7.49% 25.34% 52.70%
test1 <- predict(model_rf1, newdata = test)
table(test1, test$Hospital.overall.rating)
##
## test1 1 2 3 4 5
## 1 15 6 0 0 0
## 2 17 138 17 0 0
## 3 0 68 466 65 0
## 4 0 0 21 228 24
## 5 0 0 0 0 13
# test1 1 2 3 4 5
# 1 15 6 0 0 0
# 2 17 138 17 0 0
# 3 0 68 466 65 0
# 4 0 0 21 228 24
# 5 0 0 0 0 13
summary(model_rf1)
## Length Class Mode
## call 8 -none- call
## type 1 -none- character
## predicted 2570 factor numeric
## err.rate 3000 -none- numeric
## confusion 30 -none- numeric
## votes 12850 matrix numeric
## oob.times 2570 -none- numeric
## classes 5 -none- character
## importance 53 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 2570 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms call
conf_matrix1 <- confusionMatrix(test1, test$Hospital.overall.rating, positive = "Yes")
conf_matrix1
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 15 6 0 0 0
## 2 17 138 17 0 0
## 3 0 68 466 65 0
## 4 0 0 21 228 24
## 5 0 0 0 0 13
##
## Overall Statistics
##
## Accuracy : 0.7978
## 95% CI : (0.7725, 0.8214)
## No Information Rate : 0.4675
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6835
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.46875 0.6509 0.9246 0.7782 0.35135
## Specificity 0.99426 0.9607 0.7683 0.9427 1.00000
## Pos Pred Value 0.71429 0.8023 0.7780 0.8352 1.00000
## Neg Pred Value 0.98392 0.9183 0.9207 0.9193 0.97746
## Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
## Detection Rate 0.01391 0.1280 0.4323 0.2115 0.01206
## Detection Prevalence 0.01948 0.1596 0.5557 0.2532 0.01206
## Balanced Accuracy 0.73151 0.8058 0.8464 0.8604 0.67568
# Confusion Matrix and Statistics
#
# Reference
# Prediction 1 2 3 4 5
# 1 15 6 0 0 0
# 2 17 138 17 0 0
# 3 0 68 466 65 0
# 4 0 0 21 228 24
# 5 0 0 0 0 13
#
# Overall Statistics
#
# Accuracy : 0.7978
# 95% CI : (0.7725, 0.8214)
# No Information Rate : 0.4675
# P-Value [Acc > NIR] : < 2.2e-16
#
# Kappa : 0.6835
# Mcnemar's Test P-Value : NA
#
# Statistics by Class:
#
# Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
# Sensitivity 0.46875 0.6509 0.9246 0.7782 0.35135
# Specificity 0.99426 0.9607 0.7683 0.9427 1.00000
# Pos Pred Value 0.71429 0.8023 0.7780 0.8352 1.00000
# Neg Pred Value 0.98392 0.9183 0.9207 0.9193 0.97746
# Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
# Detection Rate 0.01391 0.1280 0.4323 0.2115 0.01206
# Detection Prevalence 0.01948 0.1596 0.5557 0.2532 0.01206
# Balanced Accuracy 0.73151 0.8058 0.8464 0.8604 0.67568
# Accuracy is 79.8%
model_rf2 <- randomForest(Hospital.overall.rating ~ ., data = train, promiximity = FALSE, ntree = 1000, mtry = 20, do.trace = TRUE, na.action = na.omit)
## ntree OOB 1 2 3 4 5
## 1: 37.38% 69.23% 48.82% 31.65% 36.69% 41.38%
## 2: 38.81% 52.17% 46.76% 33.20% 42.23% 42.55%
## 3: 36.65% 55.00% 45.19% 29.83% 41.24% 42.11%
## 4: 36.06% 52.86% 48.60% 28.06% 38.28% 55.74%
## 5: 35.12% 53.95% 45.26% 28.36% 36.35% 52.11%
## 6: 34.63% 53.85% 45.43% 26.53% 38.24% 49.32%
## 7: 33.25% 55.13% 43.83% 25.68% 35.09% 52.70%
## 8: 32.52% 52.50% 42.70% 24.76% 35.01% 54.05%
## 9: 32.65% 61.45% 43.38% 23.76% 35.95% 52.70%
## 10: 31.33% 51.81% 42.64% 22.81% 34.64% 51.35%
## 11: 30.94% 54.22% 41.36% 22.43% 34.48% 51.35%
## 12: 31.25% 52.38% 43.83% 22.33% 34.38% 51.35%
## 13: 29.89% 59.52% 40.76% 20.57% 33.43% 54.05%
## 14: 29.20% 57.14% 40.55% 19.51% 33.73% 50.00%
## 15: 28.83% 56.47% 39.07% 18.88% 34.43% 51.35%
## 16: 27.85% 55.29% 38.00% 17.54% 33.68% 55.41%
## 17: 27.41% 55.29% 38.98% 16.67% 33.38% 51.35%
## 18: 27.88% 57.65% 39.19% 17.38% 32.94% 55.41%
## 19: 26.29% 55.29% 36.86% 15.17% 32.94% 55.41%
## 20: 26.55% 56.47% 37.71% 15.47% 32.49% 56.76%
## 21: 26.16% 57.65% 38.14% 14.84% 31.89% 55.41%
## 22: 25.38% 55.29% 35.38% 14.84% 31.15% 55.41%
## 23: 24.99% 50.59% 36.23% 14.05% 31.00% 56.76%
## 24: 25.07% 51.76% 38.35% 13.50% 30.70% 56.76%
## 25: 24.63% 52.94% 36.23% 13.72% 29.51% 60.81%
## 26: 24.71% 52.94% 35.81% 13.56% 30.55% 59.46%
## 27: 24.75% 52.94% 36.86% 13.56% 29.81% 60.81%
## 28: 24.05% 56.47% 35.59% 12.54% 29.81% 58.11%
## 29: 23.97% 55.29% 35.17% 12.62% 29.51% 60.81%
## 30: 24.20% 57.65% 35.81% 13.09% 29.51% 54.05%
## 31: 24.24% 58.82% 36.86% 13.33% 28.02% 56.76%
## 32: 24.12% 58.82% 36.65% 12.85% 28.61% 56.76%
## 33: 23.93% 58.82% 35.38% 12.85% 29.06% 54.05%
## 34: 23.42% 58.82% 35.59% 12.62% 27.57% 52.70%
## 35: 23.27% 58.82% 35.59% 12.46% 27.27% 52.70%
## 36: 23.19% 60.00% 35.59% 11.67% 28.02% 55.41%
## 37: 23.77% 58.82% 36.86% 12.54% 28.17% 52.70%
## 38: 23.46% 60.00% 35.38% 12.22% 28.17% 55.41%
## 39: 23.62% 58.82% 35.81% 12.22% 28.91% 52.70%
## 40: 23.31% 55.29% 34.11% 12.22% 29.36% 52.70%
## 41: 23.39% 56.47% 35.17% 12.15% 29.06% 51.35%
## 42: 23.15% 58.82% 34.32% 11.99% 28.76% 51.35%
## 43: 22.57% 57.65% 34.53% 11.20% 28.17% 50.00%
## 44: 23.11% 58.82% 35.17% 11.67% 28.46% 52.70%
## 45: 22.84% 60.00% 34.96% 11.44% 28.02% 51.35%
## 46: 22.72% 60.00% 35.17% 11.59% 27.12% 51.35%
## 47: 22.80% 60.00% 35.81% 11.12% 27.57% 54.05%
## 48: 22.72% 60.00% 35.81% 10.96% 27.87% 51.35%
## 49: 22.30% 57.65% 34.32% 11.28% 26.83% 52.70%
## 50: 22.45% 58.82% 34.75% 10.88% 27.72% 52.70%
## 51: 22.30% 58.82% 34.32% 11.20% 26.97% 51.35%
## 52: 22.72% 60.00% 34.75% 11.36% 27.72% 52.70%
## 53: 22.45% 60.00% 33.90% 11.36% 27.12% 54.05%
## 54: 22.41% 60.00% 34.32% 11.59% 26.23% 54.05%
## 55: 22.33% 60.00% 33.69% 11.51% 26.83% 51.35%
## 56: 22.06% 58.82% 33.26% 10.96% 27.27% 51.35%
## 57: 22.26% 60.00% 33.26% 11.12% 27.27% 54.05%
## 58: 22.18% 58.82% 32.42% 11.36% 27.27% 54.05%
## 59: 22.18% 60.00% 31.36% 11.44% 27.72% 54.05%
## 60: 21.95% 60.00% 31.57% 10.80% 27.72% 55.41%
## 61: 22.18% 60.00% 31.99% 11.51% 26.97% 55.41%
## 62: 21.63% 60.00% 31.36% 10.80% 26.53% 56.76%
## 63: 21.56% 58.82% 31.57% 10.73% 26.38% 56.76%
## 64: 21.48% 57.65% 31.78% 10.41% 26.68% 56.76%
## 65: 21.87% 58.82% 31.36% 10.88% 27.42% 56.76%
## 66: 21.40% 60.00% 30.93% 10.49% 26.68% 55.41%
## 67: 21.44% 57.65% 30.72% 10.65% 26.83% 56.76%
## 68: 21.05% 56.47% 30.08% 10.25% 26.53% 58.11%
## 69: 21.13% 56.47% 29.66% 10.65% 26.68% 55.41%
## 70: 21.13% 56.47% 30.51% 10.49% 26.38% 55.41%
## 71: 21.13% 56.47% 30.08% 10.57% 26.68% 54.05%
## 72: 21.44% 60.00% 30.93% 10.65% 26.23% 58.11%
## 73: 21.48% 58.82% 30.93% 10.57% 26.68% 58.11%
## 74: 21.28% 57.65% 30.93% 10.49% 26.38% 56.76%
## 75: 21.17% 57.65% 31.36% 9.86% 26.68% 58.11%
## 76: 21.56% 58.82% 30.51% 10.41% 27.72% 56.76%
## 77: 21.48% 58.82% 30.72% 10.49% 26.97% 58.11%
## 78: 21.48% 60.00% 30.51% 10.49% 27.27% 55.41%
## 79: 21.40% 60.00% 30.72% 10.17% 27.27% 56.76%
## 80: 21.71% 61.18% 31.99% 10.33% 26.97% 58.11%
## 81: 21.56% 61.18% 31.78% 10.09% 27.12% 56.76%
## 82: 21.32% 61.18% 31.78% 9.78% 26.83% 56.76%
## 83: 21.52% 62.35% 31.36% 9.78% 27.57% 58.11%
## 84: 21.63% 62.35% 31.99% 9.86% 27.42% 58.11%
## 85: 22.10% 62.35% 32.84% 10.41% 27.57% 58.11%
## 86: 21.79% 61.18% 32.42% 10.25% 27.27% 56.76%
## 87: 21.63% 62.35% 31.99% 10.17% 27.12% 55.41%
## 88: 21.32% 62.35% 31.57% 9.78% 26.97% 55.41%
## 89: 21.44% 62.35% 31.78% 9.78% 27.12% 56.76%
## 90: 21.05% 58.82% 30.93% 9.62% 27.12% 55.41%
## 91: 21.36% 61.18% 31.78% 9.62% 27.57% 54.05%
## 92: 21.28% 62.35% 30.93% 9.70% 27.57% 54.05%
## 93: 21.48% 61.18% 31.57% 9.70% 27.87% 55.41%
## 94: 21.13% 62.35% 31.36% 9.62% 26.83% 54.05%
## 95: 21.36% 61.18% 31.36% 10.02% 27.27% 52.70%
## 96: 21.01% 61.18% 31.36% 9.54% 26.83% 52.70%
## 97: 21.17% 60.00% 31.14% 9.94% 26.97% 52.70%
## 98: 21.13% 61.18% 30.93% 9.86% 26.83% 54.05%
## 99: 21.05% 61.18% 31.36% 9.70% 26.53% 54.05%
## 100: 21.36% 58.82% 31.99% 9.86% 27.42% 52.70%
## 101: 20.97% 57.65% 32.42% 9.46% 26.53% 52.70%
## 102: 21.05% 57.65% 31.99% 9.38% 27.27% 52.70%
## 103: 20.86% 57.65% 31.99% 9.31% 26.68% 52.70%
## 104: 20.70% 57.65% 31.14% 9.23% 26.83% 52.70%
## 105: 20.89% 57.65% 31.57% 9.38% 26.97% 52.70%
## 106: 20.78% 57.65% 31.78% 9.23% 26.53% 54.05%
## 107: 21.01% 60.00% 31.78% 9.54% 26.53% 54.05%
## 108: 20.86% 60.00% 31.14% 9.46% 26.68% 52.70%
## 109: 21.05% 60.00% 31.78% 9.78% 26.38% 52.70%
## 110: 21.01% 60.00% 31.57% 9.54% 26.83% 52.70%
## 111: 20.97% 60.00% 31.78% 9.46% 26.68% 52.70%
## 112: 20.97% 60.00% 31.78% 9.62% 26.53% 51.35%
## 113: 20.82% 60.00% 31.14% 9.46% 26.68% 51.35%
## 114: 20.54% 60.00% 31.36% 9.07% 26.23% 51.35%
## 115: 20.51% 61.18% 31.36% 8.75% 26.53% 51.35%
## 116: 20.39% 61.18% 30.72% 8.91% 26.23% 51.35%
## 117: 20.70% 61.18% 31.14% 8.99% 26.97% 51.35%
## 118: 20.35% 61.18% 30.30% 8.60% 26.97% 51.35%
## 119: 20.39% 60.00% 30.51% 8.60% 27.12% 51.35%
## 120: 20.35% 58.82% 30.93% 8.44% 27.12% 51.35%
## 121: 20.31% 58.82% 30.51% 8.60% 26.97% 51.35%
## 122: 20.58% 58.82% 31.14% 8.83% 26.97% 52.70%
## 123: 20.35% 58.82% 30.72% 8.75% 26.68% 51.35%
## 124: 20.47% 58.82% 30.30% 8.83% 27.27% 51.35%
## 125: 20.70% 61.18% 30.72% 8.99% 27.12% 52.70%
## 126: 20.74% 61.18% 30.93% 8.99% 27.12% 52.70%
## 127: 20.66% 61.18% 30.30% 9.23% 26.83% 52.70%
## 128: 20.31% 61.18% 30.30% 8.83% 26.23% 52.70%
## 129: 20.54% 61.18% 30.51% 9.31% 26.08% 52.70%
## 130: 20.74% 61.18% 30.72% 9.46% 26.23% 54.05%
## 131: 20.62% 62.35% 30.08% 9.38% 26.23% 54.05%
## 132: 20.54% 62.35% 30.08% 9.23% 26.23% 54.05%
## 133: 20.43% 62.35% 30.51% 8.99% 25.93% 54.05%
## 134: 20.43% 62.35% 30.30% 9.15% 25.78% 54.05%
## 135: 20.54% 62.35% 30.51% 9.15% 26.08% 54.05%
## 136: 19.96% 62.35% 30.08% 8.52% 25.48% 52.70%
## 137: 20.19% 63.53% 29.87% 8.91% 25.63% 52.70%
## 138: 20.19% 63.53% 30.08% 8.83% 25.63% 52.70%
## 139: 20.04% 62.35% 30.08% 8.75% 25.34% 52.70%
## 140: 20.04% 61.18% 30.30% 8.52% 25.78% 52.70%
## 141: 19.96% 62.35% 29.87% 8.36% 25.93% 52.70%
## 142: 19.96% 61.18% 30.30% 8.36% 25.78% 52.70%
## 143: 20.12% 61.18% 30.08% 8.75% 25.78% 52.70%
## 144: 19.92% 61.18% 30.51% 8.28% 25.63% 52.70%
## 145: 20.04% 62.35% 30.51% 8.36% 25.78% 52.70%
## 146: 20.04% 61.18% 30.30% 8.52% 25.78% 52.70%
## 147: 20.08% 62.35% 30.72% 8.36% 25.78% 52.70%
## 148: 19.92% 61.18% 30.08% 8.28% 25.93% 52.70%
## 149: 19.77% 61.18% 30.08% 8.12% 25.63% 52.70%
## 150: 20.04% 61.18% 30.93% 8.28% 25.78% 52.70%
## 151: 19.92% 61.18% 30.30% 8.04% 26.23% 52.70%
## 152: 19.96% 61.18% 30.72% 8.12% 25.93% 52.70%
## 153: 19.96% 61.18% 30.72% 8.20% 25.93% 51.35%
## 154: 19.81% 61.18% 30.93% 8.04% 25.63% 50.00%
## 155: 19.96% 62.35% 30.72% 8.12% 25.78% 52.70%
## 156: 20.12% 61.18% 31.57% 8.20% 25.93% 51.35%
## 157: 20.31% 62.35% 31.57% 8.44% 25.93% 52.70%
## 158: 19.92% 61.18% 31.36% 7.97% 25.63% 52.70%
## 159: 20.16% 62.35% 31.14% 8.36% 25.93% 51.35%
## 160: 20.04% 62.35% 31.78% 8.04% 25.63% 51.35%
## 161: 20.23% 61.18% 31.36% 8.36% 25.78% 55.41%
## 162: 20.27% 61.18% 31.36% 8.52% 25.78% 54.05%
## 163: 20.31% 61.18% 31.57% 8.60% 25.48% 55.41%
## 164: 20.39% 61.18% 31.57% 8.60% 25.78% 55.41%
## 165: 20.31% 61.18% 31.36% 8.68% 25.63% 54.05%
## 166: 20.31% 61.18% 31.99% 8.52% 25.63% 52.70%
## 167: 20.39% 61.18% 31.78% 8.75% 25.78% 51.35%
## 168: 20.47% 60.00% 32.42% 8.83% 25.48% 52.70%
## 169: 20.27% 61.18% 31.78% 8.60% 25.48% 52.70%
## 170: 20.23% 61.18% 31.78% 8.52% 25.48% 52.70%
## 171: 20.23% 60.00% 31.99% 8.44% 25.63% 52.70%
## 172: 20.31% 60.00% 31.78% 8.52% 25.93% 52.70%
## 173: 20.31% 60.00% 32.20% 8.52% 25.63% 52.70%
## 174: 20.19% 60.00% 31.99% 8.36% 25.48% 54.05%
## 175: 20.23% 60.00% 31.99% 8.28% 25.78% 54.05%
## 176: 20.23% 60.00% 31.78% 8.44% 25.78% 52.70%
## 177: 20.23% 60.00% 32.20% 8.28% 25.78% 52.70%
## 178: 20.35% 60.00% 32.63% 8.44% 25.63% 52.70%
## 179: 20.16% 58.82% 31.99% 8.44% 25.48% 52.70%
## 180: 20.27% 60.00% 31.99% 8.60% 25.48% 52.70%
## 181: 20.31% 60.00% 31.99% 8.52% 25.78% 52.70%
## 182: 20.43% 60.00% 31.78% 8.75% 25.78% 54.05%
## 183: 20.27% 60.00% 31.36% 8.68% 25.63% 54.05%
## 184: 20.27% 57.65% 31.99% 8.60% 25.78% 52.70%
## 185: 20.47% 61.18% 31.99% 8.68% 25.78% 54.05%
## 186: 20.43% 60.00% 32.20% 8.68% 25.63% 54.05%
## 187: 20.27% 58.82% 31.78% 8.68% 25.48% 54.05%
## 188: 20.27% 60.00% 32.20% 8.52% 25.48% 52.70%
## 189: 20.27% 60.00% 31.57% 8.68% 25.63% 52.70%
## 190: 20.43% 60.00% 31.99% 8.91% 25.48% 52.70%
## 191: 20.16% 60.00% 31.36% 8.44% 25.63% 54.05%
## 192: 20.19% 58.82% 31.57% 8.44% 25.78% 54.05%
## 193: 20.08% 60.00% 31.78% 8.28% 25.34% 54.05%
## 194: 20.23% 60.00% 31.99% 8.36% 25.63% 54.05%
## 195: 20.08% 58.82% 31.36% 8.44% 25.48% 54.05%
## 196: 20.23% 58.82% 31.57% 8.60% 25.63% 54.05%
## 197: 20.08% 60.00% 31.57% 8.36% 25.34% 54.05%
## 198: 20.04% 60.00% 31.57% 8.20% 25.48% 54.05%
## 199: 20.08% 60.00% 31.78% 8.28% 25.34% 54.05%
## 200: 20.04% 60.00% 31.57% 8.20% 25.48% 54.05%
## 201: 20.31% 61.18% 32.42% 8.36% 25.63% 52.70%
## 202: 20.16% 60.00% 31.78% 8.20% 25.78% 54.05%
## 203: 20.04% 60.00% 31.78% 8.04% 25.63% 54.05%
## 204: 20.16% 60.00% 31.78% 8.28% 25.63% 54.05%
## 205: 20.27% 61.18% 31.99% 8.20% 25.93% 54.05%
## 206: 20.16% 61.18% 31.36% 8.28% 25.78% 54.05%
## 207: 19.92% 60.00% 31.36% 8.12% 25.48% 52.70%
## 208: 20.04% 60.00% 31.78% 8.28% 25.34% 52.70%
## 209: 20.04% 61.18% 31.57% 8.28% 25.34% 52.70%
## 210: 20.16% 60.00% 31.78% 8.28% 25.63% 54.05%
## 211: 20.04% 61.18% 31.78% 8.04% 25.48% 54.05%
## 212: 20.04% 61.18% 32.20% 7.97% 25.48% 52.70%
## 213: 19.96% 61.18% 31.99% 8.04% 25.19% 52.70%
## 214: 20.00% 61.18% 32.20% 7.89% 25.48% 52.70%
## 215: 19.92% 61.18% 31.57% 7.97% 25.48% 52.70%
## 216: 20.00% 61.18% 31.78% 8.04% 25.48% 52.70%
## 217: 19.92% 61.18% 31.99% 7.81% 25.48% 52.70%
## 218: 19.81% 61.18% 31.78% 7.65% 25.48% 52.70%
## 219: 20.04% 62.35% 31.99% 7.97% 25.48% 52.70%
## 220: 20.04% 61.18% 32.20% 7.89% 25.63% 52.70%
## 221: 19.88% 62.35% 31.78% 7.97% 25.04% 52.70%
## 222: 19.96% 62.35% 31.99% 7.81% 25.34% 54.05%
## 223: 19.96% 62.35% 31.99% 7.89% 25.19% 54.05%
## 224: 20.04% 62.35% 31.99% 7.81% 25.63% 54.05%
## 225: 20.08% 63.53% 31.57% 7.89% 25.78% 54.05%
## 226: 20.00% 63.53% 31.36% 8.04% 25.34% 54.05%
## 227: 20.04% 64.71% 31.36% 7.97% 25.48% 54.05%
## 228: 20.00% 63.53% 31.36% 8.04% 25.34% 54.05%
## 229: 20.04% 63.53% 31.36% 7.97% 25.63% 54.05%
## 230: 20.16% 62.35% 31.57% 8.04% 25.93% 54.05%
## 231: 20.27% 63.53% 31.78% 7.97% 26.23% 54.05%
## 232: 20.16% 63.53% 32.20% 7.89% 25.63% 54.05%
## 233: 20.19% 63.53% 32.20% 7.97% 25.63% 54.05%
## 234: 20.35% 63.53% 32.42% 8.04% 25.93% 54.05%
## 235: 20.08% 63.53% 31.36% 8.04% 25.63% 54.05%
## 236: 19.92% 63.53% 31.36% 8.04% 25.04% 54.05%
## 237: 20.16% 63.53% 31.99% 8.04% 25.48% 54.05%
## 238: 20.16% 63.53% 31.78% 8.28% 25.19% 54.05%
## 239: 20.16% 63.53% 31.99% 8.20% 25.19% 54.05%
## 240: 20.27% 63.53% 31.99% 8.20% 25.63% 54.05%
## 241: 20.23% 63.53% 31.78% 8.36% 25.34% 54.05%
## 242: 20.23% 63.53% 31.99% 8.36% 25.19% 54.05%
## 243: 20.39% 63.53% 31.57% 8.44% 25.93% 54.05%
## 244: 20.35% 63.53% 31.78% 8.44% 25.63% 54.05%
## 245: 20.08% 63.53% 31.57% 8.04% 25.48% 54.05%
## 246: 19.92% 63.53% 31.36% 7.81% 25.48% 54.05%
## 247: 20.08% 63.53% 31.57% 8.20% 25.19% 54.05%
## 248: 20.12% 63.53% 31.57% 8.20% 25.34% 54.05%
## 249: 20.12% 63.53% 31.78% 8.20% 25.19% 54.05%
## 250: 20.19% 63.53% 31.78% 8.28% 25.34% 54.05%
## 251: 20.16% 63.53% 31.99% 8.20% 25.19% 54.05%
## 252: 20.23% 63.53% 31.57% 8.52% 25.19% 54.05%
## 253: 20.00% 63.53% 31.36% 8.20% 25.04% 54.05%
## 254: 19.96% 63.53% 30.93% 8.20% 25.19% 54.05%
## 255: 20.00% 63.53% 31.36% 8.28% 24.89% 54.05%
## 256: 20.16% 63.53% 31.78% 8.12% 25.48% 54.05%
## 257: 20.19% 63.53% 31.57% 8.36% 25.34% 54.05%
## 258: 20.27% 63.53% 31.57% 8.36% 25.63% 54.05%
## 259: 20.23% 63.53% 31.57% 8.28% 25.63% 54.05%
## 260: 20.23% 63.53% 31.57% 8.44% 25.34% 54.05%
## 261: 20.16% 63.53% 32.20% 8.04% 25.34% 54.05%
## 262: 20.12% 63.53% 31.36% 8.28% 25.34% 54.05%
## 263: 20.12% 63.53% 31.14% 8.44% 25.19% 54.05%
## 264: 20.19% 62.35% 31.36% 8.44% 25.48% 54.05%
## 265: 20.00% 62.35% 30.72% 8.20% 25.63% 54.05%
## 266: 20.04% 62.35% 30.93% 8.28% 25.48% 54.05%
## 267: 20.04% 62.35% 31.36% 8.20% 25.34% 54.05%
## 268: 20.08% 62.35% 31.36% 8.12% 25.63% 54.05%
## 269: 20.12% 62.35% 31.36% 8.20% 25.63% 54.05%
## 270: 20.00% 61.18% 31.36% 8.20% 25.34% 54.05%
## 271: 19.96% 61.18% 31.14% 8.20% 25.34% 54.05%
## 272: 20.08% 61.18% 31.14% 8.28% 25.63% 54.05%
## 273: 20.08% 62.35% 31.14% 8.20% 25.63% 54.05%
## 274: 20.16% 62.35% 31.99% 8.12% 25.48% 54.05%
## 275: 19.92% 61.18% 30.93% 8.12% 25.48% 54.05%
## 276: 19.92% 61.18% 30.93% 8.12% 25.48% 54.05%
## 277: 20.00% 61.18% 31.36% 8.20% 25.34% 54.05%
## 278: 20.08% 61.18% 30.93% 8.28% 25.78% 54.05%
## 279: 20.04% 61.18% 31.36% 8.12% 25.63% 54.05%
## 280: 19.96% 61.18% 31.36% 8.12% 25.19% 55.41%
## 281: 20.08% 61.18% 31.57% 8.04% 25.63% 55.41%
## 282: 20.16% 61.18% 31.57% 8.12% 25.78% 55.41%
## 283: 20.16% 61.18% 31.78% 8.04% 25.93% 54.05%
## 284: 20.08% 61.18% 31.57% 8.12% 25.63% 54.05%
## 285: 20.27% 61.18% 31.78% 8.20% 25.93% 55.41%
## 286: 20.35% 61.18% 31.57% 8.28% 26.23% 55.41%
## 287: 20.19% 61.18% 31.57% 8.20% 25.78% 55.41%
## 288: 20.19% 60.00% 31.99% 8.12% 25.93% 54.05%
## 289: 20.31% 60.00% 31.78% 8.20% 26.23% 55.41%
## 290: 20.12% 60.00% 31.36% 8.04% 26.08% 55.41%
## 291: 20.31% 61.18% 31.36% 8.36% 26.08% 55.41%
## 292: 20.19% 62.35% 31.78% 8.12% 25.63% 55.41%
## 293: 20.35% 62.35% 31.57% 8.20% 26.23% 55.41%
## 294: 20.31% 62.35% 31.78% 8.28% 25.78% 55.41%
## 295: 20.27% 62.35% 31.78% 8.28% 25.63% 55.41%
## 296: 20.31% 62.35% 31.57% 8.20% 26.08% 55.41%
## 297: 20.23% 62.35% 31.36% 8.20% 25.93% 55.41%
## 298: 20.35% 62.35% 31.78% 8.20% 26.08% 55.41%
## 299: 20.35% 63.53% 31.99% 8.04% 26.08% 55.41%
## 300: 20.27% 62.35% 31.99% 7.97% 26.08% 55.41%
## 301: 20.23% 62.35% 31.99% 7.97% 25.93% 55.41%
## 302: 20.23% 61.18% 31.99% 8.04% 25.93% 55.41%
## 303: 20.12% 63.53% 31.36% 7.97% 25.78% 55.41%
## 304: 20.08% 61.18% 31.36% 8.20% 25.48% 55.41%
## 305: 19.96% 61.18% 31.36% 7.97% 25.48% 55.41%
## 306: 20.08% 61.18% 31.57% 8.04% 25.63% 55.41%
## 307: 20.04% 60.00% 31.36% 8.04% 25.78% 55.41%
## 308: 19.92% 61.18% 31.57% 7.73% 25.63% 55.41%
## 309: 20.04% 64.71% 31.36% 7.73% 25.78% 55.41%
## 310: 19.92% 61.18% 31.57% 7.65% 25.78% 55.41%
## 311: 19.96% 63.53% 31.57% 7.57% 25.78% 55.41%
## 312: 19.88% 63.53% 31.14% 7.65% 25.63% 55.41%
## 313: 19.92% 62.35% 30.93% 7.73% 25.93% 55.41%
## 314: 19.81% 62.35% 31.14% 7.73% 25.34% 55.41%
## 315: 19.81% 62.35% 30.93% 7.73% 25.48% 55.41%
## 316: 19.96% 64.71% 31.36% 7.73% 25.48% 55.41%
## 317: 19.73% 63.53% 30.72% 7.57% 25.48% 55.41%
## 318: 19.81% 63.53% 31.14% 7.65% 25.34% 55.41%
## 319: 19.84% 62.35% 31.14% 7.81% 25.34% 55.41%
## 320: 19.61% 61.18% 30.72% 7.65% 25.19% 55.41%
## 321: 19.69% 63.53% 30.72% 7.65% 25.19% 55.41%
## 322: 19.81% 63.53% 31.14% 7.73% 25.34% 54.05%
## 323: 19.77% 63.53% 30.72% 7.81% 25.19% 55.41%
## 324: 20.00% 63.53% 31.14% 7.97% 25.63% 54.05%
## 325: 19.96% 63.53% 31.57% 7.81% 25.48% 54.05%
## 326: 20.00% 63.53% 31.36% 7.89% 25.63% 54.05%
## 327: 19.88% 62.35% 30.93% 8.12% 25.19% 54.05%
## 328: 19.84% 62.35% 31.14% 7.73% 25.63% 54.05%
## 329: 20.00% 63.53% 31.14% 7.97% 25.63% 54.05%
## 330: 20.04% 63.53% 31.14% 7.97% 25.78% 54.05%
## 331: 19.77% 61.18% 30.93% 7.65% 25.78% 54.05%
## 332: 19.81% 61.18% 30.93% 7.65% 25.93% 54.05%
## 333: 19.81% 62.35% 31.14% 7.65% 25.63% 54.05%
## 334: 19.81% 62.35% 31.14% 7.81% 25.34% 54.05%
## 335: 19.73% 62.35% 30.93% 7.65% 25.48% 54.05%
## 336: 19.96% 62.35% 31.57% 7.65% 25.78% 55.41%
## 337: 19.96% 62.35% 31.36% 7.89% 25.48% 55.41%
## 338: 19.92% 62.35% 31.36% 7.81% 25.48% 55.41%
## 339: 19.96% 62.35% 31.14% 7.97% 25.48% 55.41%
## 340: 19.88% 62.35% 30.93% 7.97% 25.48% 54.05%
## 341: 19.77% 61.18% 30.93% 7.81% 25.34% 55.41%
## 342: 20.00% 62.35% 31.14% 8.04% 25.48% 55.41%
## 343: 19.88% 61.18% 30.93% 7.97% 25.48% 55.41%
## 344: 19.88% 61.18% 30.93% 7.89% 25.48% 56.76%
## 345: 19.96% 62.35% 30.93% 7.89% 25.63% 56.76%
## 346: 19.96% 62.35% 31.14% 7.73% 25.78% 56.76%
## 347: 20.00% 61.18% 31.14% 7.81% 25.93% 56.76%
## 348: 19.84% 60.00% 30.93% 7.81% 25.78% 55.41%
## 349: 19.88% 60.00% 31.36% 7.73% 25.78% 55.41%
## 350: 19.84% 60.00% 31.36% 7.73% 25.63% 55.41%
## 351: 19.77% 61.18% 30.93% 7.57% 25.78% 55.41%
## 352: 19.77% 60.00% 31.14% 7.57% 25.78% 55.41%
## 353: 19.69% 61.18% 30.93% 7.65% 25.34% 55.41%
## 354: 19.57% 60.00% 31.14% 7.26% 25.63% 55.41%
## 355: 19.53% 60.00% 30.72% 7.41% 25.48% 55.41%
## 356: 19.77% 61.18% 30.72% 7.65% 25.78% 55.41%
## 357: 19.69% 61.18% 31.14% 7.33% 25.78% 55.41%
## 358: 19.65% 61.18% 30.93% 7.41% 25.63% 55.41%
## 359: 19.77% 61.18% 30.93% 7.57% 25.78% 55.41%
## 360: 19.88% 62.35% 31.36% 7.65% 25.63% 55.41%
## 361: 19.69% 61.18% 31.14% 7.33% 25.78% 55.41%
## 362: 19.73% 61.18% 31.14% 7.41% 25.78% 55.41%
## 363: 19.57% 61.18% 30.72% 7.49% 25.34% 55.41%
## 364: 19.57% 61.18% 30.72% 7.57% 25.19% 55.41%
## 365: 19.61% 61.18% 30.72% 7.57% 25.34% 55.41%
## 366: 19.69% 61.18% 30.93% 7.49% 25.63% 55.41%
## 367: 19.65% 62.35% 30.51% 7.49% 25.63% 55.41%
## 368: 19.77% 62.35% 31.36% 7.49% 25.48% 55.41%
## 369: 19.73% 61.18% 30.93% 7.57% 25.63% 55.41%
## 370: 19.61% 62.35% 30.93% 7.33% 25.48% 55.41%
## 371: 19.49% 62.35% 30.93% 7.18% 25.34% 55.41%
## 372: 19.53% 62.35% 30.93% 7.26% 25.34% 55.41%
## 373: 19.46% 61.18% 30.72% 7.26% 25.34% 55.41%
## 374: 19.46% 62.35% 30.51% 7.18% 25.48% 55.41%
## 375: 19.46% 62.35% 30.30% 7.33% 25.34% 55.41%
## 376: 19.46% 61.18% 30.72% 7.33% 25.19% 55.41%
## 377: 19.53% 62.35% 30.72% 7.41% 25.19% 55.41%
## 378: 19.46% 62.35% 30.30% 7.41% 25.19% 55.41%
## 379: 19.42% 62.35% 30.51% 7.33% 25.04% 55.41%
## 380: 19.30% 61.18% 30.30% 7.33% 24.89% 55.41%
## 381: 19.46% 61.18% 30.72% 7.33% 25.19% 55.41%
## 382: 19.57% 60.00% 31.14% 7.41% 25.34% 55.41%
## 383: 19.46% 60.00% 30.93% 7.33% 25.19% 55.41%
## 384: 19.46% 60.00% 31.14% 7.26% 25.19% 55.41%
## 385: 19.49% 60.00% 30.72% 7.57% 25.19% 54.05%
## 386: 19.46% 60.00% 30.93% 7.41% 25.19% 54.05%
## 387: 19.46% 60.00% 30.93% 7.49% 25.04% 54.05%
## 388: 19.42% 60.00% 30.72% 7.49% 25.04% 54.05%
## 389: 19.49% 60.00% 31.14% 7.57% 24.89% 54.05%
## 390: 19.61% 60.00% 31.14% 7.57% 25.19% 55.41%
## 391: 19.61% 60.00% 30.93% 7.65% 25.19% 55.41%
## 392: 19.65% 60.00% 30.93% 7.73% 25.19% 55.41%
## 393: 19.61% 60.00% 31.14% 7.57% 25.19% 55.41%
## 394: 19.57% 60.00% 31.14% 7.49% 25.19% 55.41%
## 395: 19.46% 61.18% 30.72% 7.41% 25.04% 55.41%
## 396: 19.49% 60.00% 30.30% 7.49% 25.48% 55.41%
## 397: 19.53% 61.18% 30.51% 7.57% 25.19% 55.41%
## 398: 19.38% 61.18% 30.08% 7.41% 25.19% 55.41%
## 399: 19.42% 61.18% 30.08% 7.33% 25.48% 55.41%
## 400: 19.53% 62.35% 30.08% 7.41% 25.63% 55.41%
## 401: 19.57% 61.18% 30.51% 7.41% 25.63% 55.41%
## 402: 19.65% 61.18% 30.51% 7.65% 25.48% 55.41%
## 403: 19.57% 61.18% 30.72% 7.33% 25.63% 55.41%
## 404: 19.65% 61.18% 30.93% 7.49% 25.48% 55.41%
## 405: 19.49% 61.18% 30.51% 7.49% 25.19% 55.41%
## 406: 19.69% 61.18% 30.93% 7.65% 25.34% 55.41%
## 407: 19.61% 61.18% 30.30% 7.57% 25.63% 55.41%
## 408: 19.46% 61.18% 30.72% 7.33% 25.19% 55.41%
## 409: 19.53% 61.18% 30.51% 7.41% 25.48% 55.41%
## 410: 19.38% 61.18% 30.30% 7.41% 25.04% 55.41%
## 411: 19.46% 62.35% 30.72% 7.41% 24.89% 55.41%
## 412: 19.61% 62.35% 30.51% 7.41% 25.63% 55.41%
## 413: 19.65% 62.35% 30.51% 7.57% 25.48% 55.41%
## 414: 19.46% 62.35% 30.08% 7.33% 25.48% 55.41%
## 415: 19.46% 62.35% 30.08% 7.33% 25.48% 55.41%
## 416: 19.42% 62.35% 30.08% 7.41% 25.19% 55.41%
## 417: 19.61% 62.35% 30.72% 7.49% 25.34% 55.41%
## 418: 19.42% 62.35% 29.66% 7.41% 25.48% 55.41%
## 419: 19.38% 62.35% 29.87% 7.41% 25.19% 55.41%
## 420: 19.46% 62.35% 30.30% 7.33% 25.34% 55.41%
## 421: 19.42% 62.35% 30.08% 7.41% 25.19% 55.41%
## 422: 19.49% 62.35% 30.30% 7.49% 25.19% 55.41%
## 423: 19.34% 61.18% 30.08% 7.41% 25.04% 55.41%
## 424: 19.38% 62.35% 29.87% 7.41% 25.19% 55.41%
## 425: 19.38% 61.18% 30.08% 7.41% 25.19% 55.41%
## 426: 19.38% 62.35% 29.87% 7.41% 25.19% 55.41%
## 427: 19.38% 61.18% 29.87% 7.57% 25.04% 55.41%
## 428: 19.42% 62.35% 29.87% 7.49% 25.19% 55.41%
## 429: 19.26% 62.35% 29.66% 7.26% 25.19% 55.41%
## 430: 19.26% 62.35% 29.45% 7.33% 25.19% 55.41%
## 431: 19.30% 62.35% 29.87% 7.26% 25.19% 55.41%
## 432: 19.30% 62.35% 29.87% 7.26% 25.19% 55.41%
## 433: 19.18% 62.35% 29.66% 7.18% 25.19% 54.05%
## 434: 19.26% 62.35% 30.08% 7.18% 25.04% 55.41%
## 435: 19.26% 62.35% 30.08% 7.18% 25.04% 55.41%
## 436: 19.22% 62.35% 30.08% 7.10% 25.04% 55.41%
## 437: 19.26% 62.35% 29.45% 7.26% 25.34% 55.41%
## 438: 19.34% 62.35% 29.87% 7.26% 25.34% 55.41%
## 439: 19.38% 61.18% 30.08% 7.33% 25.34% 55.41%
## 440: 19.34% 62.35% 29.87% 7.26% 25.34% 55.41%
## 441: 19.26% 61.18% 29.66% 7.26% 25.34% 55.41%
## 442: 19.38% 62.35% 30.08% 7.33% 25.19% 55.41%
## 443: 19.26% 61.18% 29.87% 7.26% 25.19% 55.41%
## 444: 19.34% 62.35% 29.87% 7.26% 25.34% 55.41%
## 445: 19.30% 62.35% 29.45% 7.33% 25.34% 55.41%
## 446: 19.38% 62.35% 29.87% 7.26% 25.48% 55.41%
## 447: 19.42% 62.35% 30.08% 7.33% 25.34% 55.41%
## 448: 19.30% 62.35% 29.87% 7.33% 25.04% 55.41%
## 449: 19.34% 62.35% 29.87% 7.33% 25.19% 55.41%
## 450: 19.38% 62.35% 29.45% 7.33% 25.63% 55.41%
## 451: 19.46% 63.53% 29.66% 7.33% 25.63% 55.41%
## 452: 19.57% 62.35% 30.08% 7.41% 25.78% 55.41%
## 453: 19.42% 62.35% 30.08% 7.18% 25.63% 55.41%
## 454: 19.46% 62.35% 30.51% 7.18% 25.48% 55.41%
## 455: 19.46% 61.18% 30.72% 7.26% 25.34% 55.41%
## 456: 19.42% 61.18% 30.08% 7.26% 25.63% 55.41%
## 457: 19.38% 61.18% 29.87% 7.26% 25.63% 55.41%
## 458: 19.38% 62.35% 29.87% 7.33% 25.34% 55.41%
## 459: 19.42% 61.18% 30.08% 7.41% 25.34% 55.41%
## 460: 19.38% 62.35% 30.30% 7.18% 25.34% 55.41%
## 461: 19.42% 62.35% 30.30% 7.33% 25.19% 55.41%
## 462: 19.46% 61.18% 30.30% 7.41% 25.34% 55.41%
## 463: 19.53% 61.18% 30.51% 7.41% 25.48% 55.41%
## 464: 19.46% 61.18% 30.72% 7.26% 25.34% 55.41%
## 465: 19.49% 61.18% 30.72% 7.26% 25.48% 55.41%
## 466: 19.42% 61.18% 30.72% 7.10% 25.48% 55.41%
## 467: 19.46% 61.18% 30.51% 7.18% 25.63% 55.41%
## 468: 19.46% 60.00% 30.72% 7.18% 25.63% 55.41%
## 469: 19.61% 61.18% 30.93% 7.26% 25.78% 55.41%
## 470: 19.46% 61.18% 30.51% 7.26% 25.48% 55.41%
## 471: 19.53% 61.18% 30.93% 7.18% 25.63% 55.41%
## 472: 19.49% 61.18% 30.72% 7.26% 25.48% 55.41%
## 473: 19.57% 62.35% 30.72% 7.33% 25.48% 55.41%
## 474: 19.53% 61.18% 30.51% 7.33% 25.63% 55.41%
## 475: 19.38% 61.18% 30.08% 7.18% 25.63% 55.41%
## 476: 19.42% 61.18% 30.30% 7.18% 25.63% 55.41%
## 477: 19.57% 61.18% 30.51% 7.41% 25.63% 55.41%
## 478: 19.46% 60.00% 30.30% 7.33% 25.63% 55.41%
## 479: 19.57% 63.53% 30.08% 7.41% 25.63% 55.41%
## 480: 19.49% 61.18% 30.51% 7.26% 25.63% 55.41%
## 481: 19.57% 62.35% 30.30% 7.49% 25.48% 55.41%
## 482: 19.57% 63.53% 30.72% 7.18% 25.63% 55.41%
## 483: 19.69% 62.35% 30.72% 7.41% 25.78% 55.41%
## 484: 19.65% 62.35% 30.51% 7.33% 25.93% 55.41%
## 485: 19.42% 62.35% 30.30% 7.18% 25.48% 55.41%
## 486: 19.46% 62.35% 30.30% 7.18% 25.63% 55.41%
## 487: 19.49% 60.00% 30.72% 7.18% 25.78% 55.41%
## 488: 19.73% 61.18% 30.93% 7.41% 25.93% 55.41%
## 489: 19.61% 62.35% 30.72% 7.18% 25.93% 55.41%
## 490: 19.81% 61.18% 31.14% 7.41% 26.08% 55.41%
## 491: 19.73% 61.18% 31.14% 7.33% 25.93% 55.41%
## 492: 19.69% 62.35% 30.93% 7.33% 25.78% 55.41%
## 493: 19.61% 62.35% 30.30% 7.33% 25.93% 55.41%
## 494: 19.69% 62.35% 30.51% 7.41% 25.93% 55.41%
## 495: 19.65% 63.53% 30.51% 7.33% 25.78% 55.41%
## 496: 19.57% 62.35% 30.30% 7.26% 25.93% 55.41%
## 497: 19.53% 62.35% 30.30% 7.26% 25.78% 55.41%
## 498: 19.69% 63.53% 30.72% 7.33% 25.78% 55.41%
## 499: 19.77% 63.53% 30.72% 7.41% 25.93% 55.41%
## 500: 19.81% 63.53% 30.72% 7.41% 26.08% 55.41%
## 501: 19.77% 62.35% 30.93% 7.26% 26.23% 55.41%
## 502: 19.69% 62.35% 30.72% 7.33% 25.93% 55.41%
## 503: 19.73% 62.35% 30.93% 7.26% 26.08% 55.41%
## 504: 19.73% 62.35% 30.72% 7.33% 26.08% 55.41%
## 505: 19.65% 61.18% 30.93% 7.41% 25.63% 55.41%
## 506: 19.81% 63.53% 30.93% 7.49% 25.78% 55.41%
## 507: 19.88% 63.53% 30.93% 7.65% 25.78% 55.41%
## 508: 19.69% 62.35% 30.93% 7.49% 25.48% 55.41%
## 509: 19.69% 61.18% 31.14% 7.49% 25.48% 55.41%
## 510: 19.69% 60.00% 31.14% 7.49% 25.63% 55.41%
## 511: 19.69% 61.18% 31.14% 7.49% 25.48% 55.41%
## 512: 19.61% 61.18% 30.72% 7.49% 25.48% 55.41%
## 513: 19.73% 61.18% 30.72% 7.65% 25.63% 55.41%
## 514: 19.69% 61.18% 31.14% 7.49% 25.48% 55.41%
## 515: 19.65% 61.18% 30.72% 7.57% 25.48% 55.41%
## 516: 19.57% 61.18% 30.72% 7.26% 25.78% 55.41%
## 517: 19.69% 61.18% 30.93% 7.33% 25.93% 55.41%
## 518: 19.69% 61.18% 31.14% 7.26% 25.93% 55.41%
## 519: 19.77% 61.18% 30.93% 7.49% 25.93% 55.41%
## 520: 19.81% 61.18% 31.14% 7.57% 25.78% 55.41%
## 521: 19.84% 61.18% 30.93% 7.73% 25.78% 55.41%
## 522: 19.81% 61.18% 30.93% 7.57% 25.93% 55.41%
## 523: 19.77% 61.18% 30.93% 7.49% 25.93% 55.41%
## 524: 19.77% 61.18% 30.93% 7.49% 25.93% 55.41%
## 525: 19.77% 61.18% 30.93% 7.57% 25.78% 55.41%
## 526: 19.77% 61.18% 30.93% 7.65% 25.63% 55.41%
## 527: 19.88% 61.18% 30.93% 7.73% 25.93% 55.41%
## 528: 19.88% 61.18% 30.93% 7.73% 25.93% 55.41%
## 529: 19.84% 61.18% 30.93% 7.73% 25.78% 55.41%
## 530: 20.00% 61.18% 31.14% 7.81% 26.08% 55.41%
## 531: 19.88% 61.18% 30.93% 7.65% 26.08% 55.41%
## 532: 19.92% 61.18% 30.93% 7.81% 25.93% 55.41%
## 533: 19.88% 61.18% 31.14% 7.65% 25.93% 55.41%
## 534: 19.88% 61.18% 31.14% 7.57% 26.08% 55.41%
## 535: 19.84% 61.18% 31.14% 7.57% 25.93% 55.41%
## 536: 19.81% 61.18% 30.93% 7.49% 25.93% 56.76%
## 537: 19.81% 61.18% 30.72% 7.49% 26.08% 56.76%
## 538: 19.84% 61.18% 30.72% 7.57% 26.08% 56.76%
## 539: 19.73% 61.18% 30.30% 7.41% 26.23% 56.76%
## 540: 19.77% 61.18% 30.30% 7.57% 26.08% 56.76%
## 541: 19.81% 61.18% 30.30% 7.57% 26.23% 56.76%
## 542: 19.73% 61.18% 30.30% 7.49% 26.08% 56.76%
## 543: 19.77% 61.18% 30.51% 7.41% 26.23% 56.76%
## 544: 19.84% 61.18% 30.72% 7.57% 26.08% 56.76%
## 545: 19.69% 61.18% 30.30% 7.49% 25.93% 56.76%
## 546: 19.77% 61.18% 30.51% 7.49% 26.08% 56.76%
## 547: 19.81% 61.18% 30.93% 7.41% 26.08% 56.76%
## 548: 19.84% 61.18% 31.36% 7.33% 26.08% 56.76%
## 549: 19.77% 61.18% 31.14% 7.26% 26.08% 56.76%
## 550: 19.73% 61.18% 30.72% 7.33% 26.08% 56.76%
## 551: 19.77% 61.18% 31.14% 7.26% 26.08% 56.76%
## 552: 19.88% 61.18% 30.93% 7.57% 26.08% 56.76%
## 553: 19.88% 61.18% 31.14% 7.41% 26.23% 56.76%
## 554: 19.84% 61.18% 31.14% 7.41% 26.23% 55.41%
## 555: 19.77% 61.18% 30.93% 7.41% 25.93% 56.76%
## 556: 19.88% 61.18% 31.14% 7.41% 26.23% 56.76%
## 557: 19.81% 61.18% 30.93% 7.41% 26.08% 56.76%
## 558: 19.73% 61.18% 31.14% 7.18% 26.08% 56.76%
## 559: 19.84% 61.18% 31.36% 7.33% 26.08% 56.76%
## 560: 19.81% 61.18% 31.36% 7.26% 26.08% 56.76%
## 561: 19.77% 60.00% 31.36% 7.26% 26.08% 56.76%
## 562: 19.84% 62.35% 31.36% 7.18% 26.23% 56.76%
## 563: 19.77% 60.00% 31.14% 7.18% 26.38% 56.76%
## 564: 19.73% 60.00% 31.36% 7.26% 25.93% 56.76%
## 565: 19.69% 60.00% 31.36% 7.10% 26.08% 56.76%
## 566: 19.61% 60.00% 31.14% 7.10% 25.93% 56.76%
## 567: 19.69% 61.18% 31.14% 7.18% 25.93% 56.76%
## 568: 19.84% 61.18% 31.14% 7.33% 26.23% 56.76%
## 569: 19.88% 61.18% 31.36% 7.33% 26.23% 56.76%
## 570: 19.88% 60.00% 31.36% 7.33% 26.38% 56.76%
## 571: 19.84% 60.00% 31.14% 7.33% 26.38% 56.76%
## 572: 19.77% 60.00% 30.51% 7.33% 26.53% 56.76%
## 573: 19.69% 60.00% 30.30% 7.33% 26.38% 56.76%
## 574: 19.61% 60.00% 30.72% 7.10% 26.23% 56.76%
## 575: 19.69% 60.00% 30.93% 7.10% 26.38% 56.76%
## 576: 19.65% 60.00% 30.72% 7.10% 26.38% 56.76%
## 577: 19.73% 60.00% 30.93% 7.18% 26.38% 56.76%
## 578: 19.84% 60.00% 31.14% 7.33% 26.38% 56.76%
## 579: 19.61% 60.00% 30.72% 7.18% 26.08% 56.76%
## 580: 19.73% 60.00% 30.93% 7.26% 26.23% 56.76%
## 581: 19.57% 60.00% 30.51% 7.18% 26.08% 56.76%
## 582: 19.69% 60.00% 30.93% 7.18% 26.23% 56.76%
## 583: 19.81% 60.00% 30.72% 7.33% 26.53% 56.76%
## 584: 19.81% 60.00% 30.51% 7.41% 26.53% 56.76%
## 585: 19.81% 60.00% 30.72% 7.33% 26.53% 56.76%
## 586: 19.81% 60.00% 30.72% 7.33% 26.53% 56.76%
## 587: 19.77% 60.00% 30.72% 7.33% 26.38% 56.76%
## 588: 19.77% 60.00% 30.93% 7.26% 26.38% 56.76%
## 589: 19.81% 60.00% 30.93% 7.33% 26.38% 56.76%
## 590: 19.77% 60.00% 30.93% 7.26% 26.38% 56.76%
## 591: 19.84% 60.00% 30.93% 7.41% 26.38% 56.76%
## 592: 19.84% 60.00% 31.14% 7.26% 26.53% 56.76%
## 593: 19.84% 60.00% 30.93% 7.33% 26.53% 56.76%
## 594: 19.81% 60.00% 31.14% 7.26% 26.38% 56.76%
## 595: 19.81% 60.00% 31.14% 7.26% 26.38% 56.76%
## 596: 19.84% 60.00% 31.14% 7.26% 26.53% 56.76%
## 597: 19.84% 60.00% 31.14% 7.26% 26.53% 56.76%
## 598: 19.88% 60.00% 31.36% 7.26% 26.53% 56.76%
## 599: 19.96% 60.00% 31.78% 7.26% 26.53% 56.76%
## 600: 20.00% 60.00% 31.78% 7.33% 26.53% 56.76%
## 601: 20.00% 60.00% 31.78% 7.41% 26.38% 56.76%
## 602: 20.00% 60.00% 31.57% 7.49% 26.38% 56.76%
## 603: 19.81% 60.00% 31.57% 7.33% 25.93% 56.76%
## 604: 19.84% 60.00% 31.57% 7.33% 26.08% 56.76%
## 605: 19.84% 60.00% 31.57% 7.33% 26.08% 56.76%
## 606: 19.84% 60.00% 31.57% 7.33% 26.08% 56.76%
## 607: 19.92% 60.00% 31.57% 7.33% 26.38% 56.76%
## 608: 19.96% 60.00% 31.78% 7.33% 26.38% 56.76%
## 609: 19.96% 61.18% 31.57% 7.41% 26.23% 56.76%
## 610: 20.00% 60.00% 31.57% 7.49% 26.38% 56.76%
## 611: 20.00% 60.00% 31.78% 7.49% 26.23% 56.76%
## 612: 19.96% 60.00% 31.57% 7.41% 26.38% 56.76%
## 613: 20.04% 60.00% 31.57% 7.49% 26.53% 56.76%
## 614: 19.96% 60.00% 31.57% 7.41% 26.38% 56.76%
## 615: 19.84% 58.82% 31.36% 7.33% 26.38% 56.76%
## 616: 19.81% 60.00% 31.14% 7.26% 26.38% 56.76%
## 617: 19.81% 60.00% 31.57% 7.26% 26.08% 56.76%
## 618: 19.73% 60.00% 31.57% 7.18% 25.93% 56.76%
## 619: 19.81% 61.18% 31.57% 7.18% 26.08% 56.76%
## 620: 19.77% 62.35% 31.14% 7.26% 25.93% 56.76%
## 621: 19.73% 62.35% 31.14% 7.10% 26.08% 56.76%
## 622: 19.73% 62.35% 30.72% 7.26% 26.08% 56.76%
## 623: 19.73% 62.35% 31.36% 7.10% 25.93% 56.76%
## 624: 19.77% 63.53% 31.36% 7.10% 25.93% 56.76%
## 625: 19.92% 63.53% 31.14% 7.18% 26.53% 56.76%
## 626: 19.73% 62.35% 31.14% 7.02% 26.23% 56.76%
## 627: 19.84% 62.35% 31.36% 7.10% 26.38% 56.76%
## 628: 19.77% 62.35% 31.36% 7.10% 26.08% 56.76%
## 629: 19.69% 62.35% 30.93% 7.18% 25.93% 56.76%
## 630: 19.65% 61.18% 30.93% 7.10% 26.08% 56.76%
## 631: 19.65% 61.18% 31.36% 7.02% 25.93% 56.76%
## 632: 19.65% 61.18% 31.36% 7.02% 25.93% 56.76%
## 633: 19.65% 62.35% 31.14% 7.02% 25.93% 56.76%
## 634: 19.53% 62.35% 31.14% 7.02% 25.48% 56.76%
## 635: 19.53% 62.35% 31.14% 7.10% 25.34% 56.76%
## 636: 19.53% 62.35% 31.14% 7.02% 25.48% 56.76%
## 637: 19.77% 62.35% 30.93% 7.18% 26.23% 56.76%
## 638: 19.57% 62.35% 31.14% 7.10% 25.48% 56.76%
## 639: 19.69% 61.18% 31.36% 7.18% 25.78% 56.76%
## 640: 19.42% 62.35% 30.72% 7.02% 25.34% 56.76%
## 641: 19.49% 62.35% 31.14% 6.94% 25.48% 56.76%
## 642: 19.65% 62.35% 31.36% 7.02% 25.78% 56.76%
## 643: 19.61% 62.35% 31.36% 7.10% 25.48% 56.76%
## 644: 19.77% 62.35% 31.36% 7.18% 25.93% 56.76%
## 645: 19.77% 62.35% 31.36% 7.10% 26.08% 56.76%
## 646: 19.69% 62.35% 30.93% 7.10% 26.08% 56.76%
## 647: 19.61% 61.18% 31.14% 7.10% 25.78% 56.76%
## 648: 19.53% 61.18% 30.93% 7.10% 25.63% 56.76%
## 649: 19.42% 60.00% 30.72% 7.02% 25.63% 56.76%
## 650: 19.49% 61.18% 30.72% 7.10% 25.63% 56.76%
## 651: 19.61% 60.00% 30.93% 7.10% 26.08% 56.76%
## 652: 19.61% 60.00% 31.14% 7.02% 26.08% 56.76%
## 653: 19.53% 60.00% 31.14% 7.10% 25.63% 56.76%
## 654: 19.53% 60.00% 30.93% 7.10% 25.78% 56.76%
## 655: 19.46% 60.00% 30.93% 7.10% 25.48% 56.76%
## 656: 19.46% 60.00% 30.93% 7.10% 25.48% 56.76%
## 657: 19.42% 60.00% 31.14% 7.02% 25.34% 56.76%
## 658: 19.42% 60.00% 31.14% 6.94% 25.48% 56.76%
## 659: 19.61% 60.00% 31.14% 7.10% 25.93% 56.76%
## 660: 19.53% 60.00% 31.14% 7.10% 25.78% 55.41%
## 661: 19.61% 60.00% 31.14% 7.10% 25.93% 56.76%
## 662: 19.61% 60.00% 31.36% 7.10% 25.78% 56.76%
## 663: 19.53% 60.00% 30.93% 7.10% 25.78% 56.76%
## 664: 19.49% 60.00% 30.93% 7.02% 25.78% 56.76%
## 665: 19.53% 60.00% 31.14% 7.02% 25.78% 56.76%
## 666: 19.57% 60.00% 31.14% 7.02% 25.93% 56.76%
## 667: 19.53% 61.18% 31.14% 6.94% 25.78% 56.76%
## 668: 19.53% 61.18% 31.14% 6.94% 25.78% 56.76%
## 669: 19.53% 61.18% 31.14% 7.02% 25.78% 55.41%
## 670: 19.57% 62.35% 31.14% 7.02% 25.78% 55.41%
## 671: 19.57% 61.18% 30.93% 6.94% 26.08% 56.76%
## 672: 19.73% 61.18% 31.14% 7.18% 26.08% 56.76%
## 673: 19.77% 62.35% 31.36% 7.18% 25.93% 56.76%
## 674: 19.61% 62.35% 31.36% 7.02% 25.63% 56.76%
## 675: 19.49% 61.18% 31.14% 7.02% 25.63% 55.41%
## 676: 19.61% 61.18% 31.14% 7.10% 25.93% 55.41%
## 677: 19.69% 61.18% 30.93% 7.18% 26.23% 55.41%
## 678: 19.77% 61.18% 31.14% 7.18% 26.38% 55.41%
## 679: 19.69% 61.18% 30.93% 7.26% 26.08% 55.41%
## 680: 19.57% 61.18% 30.93% 7.18% 25.78% 55.41%
## 681: 19.61% 60.00% 31.36% 7.18% 25.78% 55.41%
## 682: 19.65% 60.00% 31.14% 7.18% 26.08% 55.41%
## 683: 19.65% 60.00% 31.57% 7.18% 25.63% 56.76%
## 684: 19.65% 60.00% 31.36% 7.10% 25.93% 56.76%
## 685: 19.57% 60.00% 31.36% 7.18% 25.63% 55.41%
## 686: 19.61% 60.00% 31.36% 7.18% 25.78% 55.41%
## 687: 19.53% 61.18% 30.72% 7.18% 25.78% 55.41%
## 688: 19.57% 61.18% 30.93% 7.18% 25.78% 55.41%
## 689: 19.65% 61.18% 31.14% 7.18% 25.93% 55.41%
## 690: 19.69% 61.18% 31.36% 7.18% 25.93% 55.41%
## 691: 19.65% 60.00% 30.93% 7.26% 26.08% 55.41%
## 692: 19.61% 61.18% 30.93% 7.02% 26.08% 56.76%
## 693: 19.57% 61.18% 30.72% 7.02% 26.08% 56.76%
## 694: 19.77% 62.35% 31.14% 7.18% 26.08% 56.76%
## 695: 19.61% 60.00% 30.93% 7.18% 25.93% 56.76%
## 696: 19.73% 61.18% 31.14% 7.18% 26.08% 56.76%
## 697: 19.65% 61.18% 30.93% 7.10% 26.08% 56.76%
## 698: 19.69% 60.00% 31.14% 7.18% 26.08% 56.76%
## 699: 19.73% 61.18% 31.14% 7.18% 26.08% 56.76%
## 700: 19.88% 62.35% 31.36% 7.26% 26.23% 56.76%
## 701: 19.81% 61.18% 31.36% 7.18% 26.23% 56.76%
## 702: 19.81% 61.18% 31.14% 7.33% 26.08% 56.76%
## 703: 19.96% 62.35% 31.57% 7.33% 26.23% 56.76%
## 704: 19.88% 62.35% 31.14% 7.33% 26.23% 56.76%
## 705: 19.77% 62.35% 30.72% 7.33% 26.08% 56.76%
## 706: 19.81% 62.35% 31.14% 7.33% 25.93% 56.76%
## 707: 19.73% 62.35% 30.93% 7.26% 26.08% 55.41%
## 708: 19.69% 61.18% 30.93% 7.26% 25.93% 56.76%
## 709: 19.73% 62.35% 30.93% 7.26% 25.93% 56.76%
## 710: 19.69% 62.35% 30.93% 7.18% 26.08% 55.41%
## 711: 19.65% 62.35% 30.93% 7.18% 25.93% 55.41%
## 712: 19.69% 61.18% 30.93% 7.18% 26.08% 56.76%
## 713: 19.77% 62.35% 31.14% 7.18% 26.08% 56.76%
## 714: 19.69% 61.18% 30.93% 7.33% 25.93% 55.41%
## 715: 19.77% 61.18% 31.14% 7.33% 25.93% 56.76%
## 716: 19.92% 62.35% 31.14% 7.41% 26.23% 56.76%
## 717: 19.77% 61.18% 31.14% 7.41% 25.93% 55.41%
## 718: 19.73% 61.18% 31.14% 7.33% 25.93% 55.41%
## 719: 19.77% 61.18% 31.57% 7.26% 25.93% 55.41%
## 720: 19.65% 61.18% 31.14% 7.33% 25.63% 55.41%
## 721: 19.57% 61.18% 30.93% 7.26% 25.63% 55.41%
## 722: 19.69% 61.18% 31.14% 7.33% 25.78% 55.41%
## 723: 19.61% 61.18% 30.93% 7.26% 25.78% 55.41%
## 724: 19.61% 61.18% 31.14% 7.18% 25.78% 55.41%
## 725: 19.65% 61.18% 30.72% 7.26% 26.08% 55.41%
## 726: 19.42% 61.18% 30.30% 7.18% 25.63% 55.41%
## 727: 19.46% 61.18% 30.30% 7.10% 25.93% 55.41%
## 728: 19.57% 61.18% 30.93% 7.18% 25.78% 55.41%
## 729: 19.61% 61.18% 30.72% 7.26% 25.93% 55.41%
## 730: 19.65% 61.18% 30.72% 7.41% 25.78% 55.41%
## 731: 19.73% 61.18% 30.93% 7.41% 25.93% 55.41%
## 732: 19.65% 61.18% 30.72% 7.33% 25.93% 55.41%
## 733: 19.65% 61.18% 30.72% 7.49% 25.63% 55.41%
## 734: 19.61% 61.18% 30.72% 7.41% 25.63% 55.41%
## 735: 19.69% 61.18% 31.14% 7.33% 25.78% 55.41%
## 736: 19.61% 61.18% 30.93% 7.33% 25.63% 55.41%
## 737: 19.61% 61.18% 30.72% 7.41% 25.63% 55.41%
## 738: 19.53% 61.18% 30.51% 7.33% 25.63% 55.41%
## 739: 19.61% 61.18% 30.72% 7.41% 25.63% 55.41%
## 740: 19.61% 61.18% 30.72% 7.33% 25.78% 55.41%
## 741: 19.65% 61.18% 31.14% 7.33% 25.63% 55.41%
## 742: 19.61% 61.18% 30.72% 7.41% 25.63% 55.41%
## 743: 19.46% 61.18% 30.30% 7.33% 25.48% 55.41%
## 744: 19.49% 61.18% 30.72% 7.26% 25.48% 55.41%
## 745: 19.46% 60.00% 30.51% 7.26% 25.63% 55.41%
## 746: 19.46% 60.00% 30.72% 7.26% 25.48% 55.41%
## 747: 19.53% 61.18% 30.93% 7.26% 25.48% 55.41%
## 748: 19.49% 61.18% 31.14% 7.18% 25.34% 55.41%
## 749: 19.42% 61.18% 30.30% 7.26% 25.48% 55.41%
## 750: 19.42% 61.18% 30.51% 7.26% 25.34% 55.41%
## 751: 19.49% 61.18% 30.93% 7.18% 25.48% 55.41%
## 752: 19.42% 61.18% 30.72% 7.10% 25.48% 55.41%
## 753: 19.46% 61.18% 30.72% 7.26% 25.34% 55.41%
## 754: 19.53% 61.18% 30.93% 7.33% 25.34% 55.41%
## 755: 19.46% 61.18% 30.93% 7.26% 25.19% 55.41%
## 756: 19.46% 61.18% 30.72% 7.26% 25.34% 55.41%
## 757: 19.46% 61.18% 30.93% 7.26% 25.19% 55.41%
## 758: 19.53% 61.18% 30.72% 7.33% 25.48% 55.41%
## 759: 19.42% 61.18% 30.72% 7.26% 25.19% 55.41%
## 760: 19.42% 61.18% 30.72% 7.26% 25.19% 55.41%
## 761: 19.46% 61.18% 30.51% 7.26% 25.48% 55.41%
## 762: 19.34% 61.18% 30.30% 7.26% 25.19% 55.41%
## 763: 19.38% 61.18% 30.30% 7.33% 25.19% 55.41%
## 764: 19.38% 61.18% 30.30% 7.33% 25.19% 55.41%
## 765: 19.38% 61.18% 30.08% 7.33% 25.34% 55.41%
## 766: 19.34% 61.18% 30.30% 7.26% 25.19% 55.41%
## 767: 19.30% 61.18% 30.30% 7.26% 25.04% 55.41%
## 768: 19.30% 61.18% 30.08% 7.26% 25.19% 55.41%
## 769: 19.30% 61.18% 30.30% 7.26% 25.04% 55.41%
## 770: 19.42% 61.18% 30.72% 7.26% 25.19% 55.41%
## 771: 19.30% 61.18% 30.08% 7.26% 25.19% 55.41%
## 772: 19.26% 61.18% 30.08% 7.26% 25.04% 55.41%
## 773: 19.42% 61.18% 30.72% 7.18% 25.34% 55.41%
## 774: 19.26% 61.18% 30.30% 7.18% 25.04% 55.41%
## 775: 19.34% 61.18% 30.51% 7.18% 25.19% 55.41%
## 776: 19.22% 61.18% 30.08% 7.18% 25.04% 55.41%
## 777: 19.22% 61.18% 30.30% 7.18% 24.89% 55.41%
## 778: 19.18% 61.18% 30.08% 7.18% 24.89% 55.41%
## 779: 19.26% 61.18% 30.30% 7.18% 25.04% 55.41%
## 780: 19.22% 61.18% 30.08% 7.18% 25.04% 55.41%
## 781: 19.18% 61.18% 29.87% 7.18% 25.04% 55.41%
## 782: 19.18% 61.18% 29.87% 7.10% 25.19% 55.41%
## 783: 19.30% 61.18% 30.08% 7.18% 25.34% 55.41%
## 784: 19.18% 61.18% 29.87% 7.10% 25.19% 55.41%
## 785: 19.22% 61.18% 30.08% 7.18% 25.04% 55.41%
## 786: 19.22% 61.18% 29.87% 7.18% 25.19% 55.41%
## 787: 19.22% 61.18% 30.08% 7.10% 25.19% 55.41%
## 788: 19.34% 61.18% 30.08% 7.18% 25.48% 55.41%
## 789: 19.26% 61.18% 30.30% 7.02% 25.34% 55.41%
## 790: 19.22% 61.18% 29.87% 7.10% 25.34% 55.41%
## 791: 19.26% 61.18% 30.08% 7.10% 25.34% 55.41%
## 792: 19.26% 61.18% 30.08% 7.02% 25.48% 55.41%
## 793: 19.38% 61.18% 30.51% 7.10% 25.48% 55.41%
## 794: 19.22% 61.18% 29.87% 7.10% 25.34% 55.41%
## 795: 19.26% 61.18% 29.87% 7.18% 25.34% 55.41%
## 796: 19.30% 61.18% 29.87% 7.18% 25.48% 55.41%
## 797: 19.38% 61.18% 30.30% 7.18% 25.48% 55.41%
## 798: 19.30% 61.18% 29.87% 7.18% 25.48% 55.41%
## 799: 19.34% 61.18% 30.30% 7.02% 25.63% 55.41%
## 800: 19.26% 61.18% 30.30% 7.02% 25.34% 55.41%
## 801: 19.42% 61.18% 30.30% 7.18% 25.63% 55.41%
## 802: 19.38% 61.18% 30.30% 7.18% 25.48% 55.41%
## 803: 19.30% 61.18% 29.87% 7.18% 25.48% 55.41%
## 804: 19.34% 61.18% 29.87% 7.18% 25.63% 55.41%
## 805: 19.38% 61.18% 30.30% 7.10% 25.63% 55.41%
## 806: 19.38% 61.18% 30.08% 7.18% 25.63% 55.41%
## 807: 19.38% 61.18% 30.08% 7.18% 25.63% 55.41%
## 808: 19.38% 61.18% 30.08% 7.26% 25.48% 55.41%
## 809: 19.42% 61.18% 30.08% 7.26% 25.63% 55.41%
## 810: 19.38% 61.18% 30.08% 7.26% 25.48% 55.41%
## 811: 19.26% 61.18% 30.08% 7.10% 25.34% 55.41%
## 812: 19.30% 61.18% 30.08% 7.18% 25.34% 55.41%
## 813: 19.34% 61.18% 30.30% 7.18% 25.34% 55.41%
## 814: 19.30% 61.18% 30.08% 7.18% 25.34% 55.41%
## 815: 19.30% 61.18% 30.08% 7.18% 25.34% 55.41%
## 816: 19.34% 61.18% 30.08% 7.33% 25.19% 55.41%
## 817: 19.38% 61.18% 30.30% 7.26% 25.34% 55.41%
## 818: 19.49% 61.18% 30.51% 7.33% 25.48% 55.41%
## 819: 19.46% 61.18% 30.51% 7.26% 25.48% 55.41%
## 820: 19.46% 61.18% 30.30% 7.33% 25.48% 55.41%
## 821: 19.42% 61.18% 30.30% 7.26% 25.48% 55.41%
## 822: 19.42% 61.18% 30.30% 7.33% 25.34% 55.41%
## 823: 19.46% 61.18% 30.08% 7.33% 25.63% 55.41%
## 824: 19.49% 61.18% 30.30% 7.26% 25.78% 55.41%
## 825: 19.38% 61.18% 30.08% 7.18% 25.63% 55.41%
## 826: 19.38% 61.18% 30.08% 7.18% 25.63% 55.41%
## 827: 19.38% 61.18% 30.30% 7.18% 25.48% 55.41%
## 828: 19.42% 61.18% 30.08% 7.26% 25.63% 55.41%
## 829: 19.42% 61.18% 30.08% 7.18% 25.78% 55.41%
## 830: 19.49% 61.18% 30.08% 7.33% 25.78% 55.41%
## 831: 19.46% 61.18% 30.08% 7.26% 25.78% 55.41%
## 832: 19.46% 61.18% 30.30% 7.18% 25.78% 55.41%
## 833: 19.46% 61.18% 29.87% 7.41% 25.63% 55.41%
## 834: 19.46% 61.18% 30.30% 7.26% 25.63% 55.41%
## 835: 19.46% 61.18% 30.08% 7.33% 25.63% 55.41%
## 836: 19.42% 61.18% 29.87% 7.33% 25.63% 55.41%
## 837: 19.49% 61.18% 30.08% 7.26% 25.93% 55.41%
## 838: 19.57% 61.18% 29.87% 7.41% 26.08% 55.41%
## 839: 19.53% 61.18% 30.51% 7.18% 25.93% 55.41%
## 840: 19.57% 61.18% 30.51% 7.26% 25.93% 55.41%
## 841: 19.42% 61.18% 29.87% 7.26% 25.78% 55.41%
## 842: 19.53% 61.18% 30.30% 7.33% 25.78% 55.41%
## 843: 19.49% 61.18% 30.08% 7.33% 25.78% 55.41%
## 844: 19.49% 60.00% 30.08% 7.33% 25.93% 55.41%
## 845: 19.49% 60.00% 30.30% 7.41% 25.63% 55.41%
## 846: 19.49% 60.00% 30.08% 7.41% 25.78% 55.41%
## 847: 19.46% 60.00% 30.08% 7.41% 25.63% 55.41%
## 848: 19.49% 60.00% 30.08% 7.49% 25.63% 55.41%
## 849: 19.49% 61.18% 30.30% 7.33% 25.63% 55.41%
## 850: 19.57% 61.18% 30.30% 7.41% 25.78% 55.41%
## 851: 19.53% 61.18% 30.30% 7.33% 25.78% 55.41%
## 852: 19.61% 61.18% 30.51% 7.33% 25.93% 55.41%
## 853: 19.65% 61.18% 30.72% 7.33% 25.93% 55.41%
## 854: 19.61% 61.18% 30.72% 7.33% 25.78% 55.41%
## 855: 19.61% 62.35% 30.72% 7.41% 25.48% 55.41%
## 856: 19.57% 62.35% 30.51% 7.33% 25.63% 55.41%
## 857: 19.61% 62.35% 30.51% 7.41% 25.63% 55.41%
## 858: 19.61% 62.35% 30.51% 7.49% 25.48% 55.41%
## 859: 19.61% 62.35% 30.51% 7.49% 25.48% 55.41%
## 860: 19.57% 62.35% 30.51% 7.41% 25.48% 55.41%
## 861: 19.61% 62.35% 30.72% 7.41% 25.48% 55.41%
## 862: 19.65% 62.35% 30.72% 7.49% 25.48% 55.41%
## 863: 19.65% 62.35% 30.72% 7.49% 25.48% 55.41%
## 864: 19.69% 62.35% 30.72% 7.49% 25.63% 55.41%
## 865: 19.69% 62.35% 30.72% 7.49% 25.63% 55.41%
## 866: 19.73% 62.35% 30.72% 7.57% 25.63% 55.41%
## 867: 19.61% 62.35% 30.51% 7.41% 25.63% 55.41%
## 868: 19.69% 62.35% 30.51% 7.49% 25.78% 55.41%
## 869: 19.61% 62.35% 30.72% 7.33% 25.63% 55.41%
## 870: 19.61% 62.35% 30.51% 7.41% 25.63% 55.41%
## 871: 19.57% 62.35% 30.72% 7.41% 25.34% 55.41%
## 872: 19.65% 62.35% 30.51% 7.33% 25.93% 55.41%
## 873: 19.61% 62.35% 30.51% 7.33% 25.78% 55.41%
## 874: 19.61% 62.35% 30.51% 7.33% 25.78% 55.41%
## 875: 19.65% 62.35% 30.51% 7.41% 25.78% 55.41%
## 876: 19.57% 62.35% 30.51% 7.26% 25.78% 55.41%
## 877: 19.57% 62.35% 30.51% 7.26% 25.78% 55.41%
## 878: 19.53% 62.35% 30.51% 7.26% 25.63% 55.41%
## 879: 19.61% 62.35% 30.51% 7.33% 25.78% 55.41%
## 880: 19.49% 61.18% 30.51% 7.18% 25.78% 55.41%
## 881: 19.57% 62.35% 30.51% 7.26% 25.78% 55.41%
## 882: 19.53% 62.35% 30.51% 7.26% 25.63% 55.41%
## 883: 19.49% 61.18% 30.51% 7.18% 25.78% 55.41%
## 884: 19.49% 62.35% 30.51% 7.18% 25.63% 55.41%
## 885: 19.46% 61.18% 30.51% 7.18% 25.63% 55.41%
## 886: 19.42% 61.18% 30.51% 7.10% 25.63% 55.41%
## 887: 19.46% 61.18% 30.51% 7.18% 25.63% 55.41%
## 888: 19.46% 62.35% 30.51% 7.02% 25.78% 55.41%
## 889: 19.46% 62.35% 30.51% 7.02% 25.78% 55.41%
## 890: 19.46% 62.35% 30.51% 7.02% 25.78% 55.41%
## 891: 19.34% 61.18% 30.51% 6.94% 25.63% 55.41%
## 892: 19.38% 61.18% 30.51% 7.02% 25.63% 55.41%
## 893: 19.46% 61.18% 30.30% 7.10% 25.93% 55.41%
## 894: 19.42% 61.18% 30.51% 7.02% 25.78% 55.41%
## 895: 19.46% 61.18% 30.51% 7.02% 25.93% 55.41%
## 896: 19.53% 61.18% 30.51% 7.18% 25.93% 55.41%
## 897: 19.46% 61.18% 30.51% 7.02% 25.93% 55.41%
## 898: 19.38% 61.18% 30.51% 6.94% 25.78% 55.41%
## 899: 19.49% 61.18% 30.51% 7.18% 25.78% 55.41%
## 900: 19.53% 61.18% 30.51% 7.18% 25.93% 55.41%
## 901: 19.42% 61.18% 30.51% 7.02% 25.78% 55.41%
## 902: 19.49% 61.18% 30.51% 7.18% 25.78% 55.41%
## 903: 19.42% 61.18% 30.51% 7.02% 25.78% 55.41%
## 904: 19.42% 61.18% 30.51% 7.02% 25.78% 55.41%
## 905: 19.42% 61.18% 30.51% 7.02% 25.78% 55.41%
## 906: 19.46% 61.18% 30.51% 7.10% 25.78% 55.41%
## 907: 19.46% 61.18% 30.51% 7.26% 25.63% 54.05%
## 908: 19.46% 61.18% 30.51% 7.18% 25.78% 54.05%
## 909: 19.53% 61.18% 30.51% 7.18% 25.93% 55.41%
## 910: 19.49% 61.18% 30.51% 7.10% 25.93% 55.41%
## 911: 19.46% 61.18% 30.30% 7.10% 25.93% 55.41%
## 912: 19.46% 61.18% 30.30% 7.26% 25.63% 55.41%
## 913: 19.42% 61.18% 30.30% 7.18% 25.78% 54.05%
## 914: 19.38% 61.18% 30.30% 7.10% 25.78% 54.05%
## 915: 19.38% 61.18% 30.30% 7.18% 25.63% 54.05%
## 916: 19.49% 61.18% 30.30% 7.26% 25.78% 55.41%
## 917: 19.30% 61.18% 30.08% 7.10% 25.63% 54.05%
## 918: 19.30% 61.18% 30.30% 7.10% 25.48% 54.05%
## 919: 19.42% 61.18% 30.51% 7.10% 25.78% 54.05%
## 920: 19.42% 62.35% 30.51% 7.10% 25.63% 54.05%
## 921: 19.42% 62.35% 30.30% 7.18% 25.63% 54.05%
## 922: 19.38% 61.18% 30.30% 7.18% 25.63% 54.05%
## 923: 19.30% 61.18% 30.30% 7.10% 25.48% 54.05%
## 924: 19.42% 61.18% 30.51% 7.26% 25.48% 54.05%
## 925: 19.34% 61.18% 30.30% 7.18% 25.48% 54.05%
## 926: 19.26% 61.18% 30.08% 7.18% 25.34% 54.05%
## 927: 19.22% 61.18% 30.08% 7.10% 25.34% 54.05%
## 928: 19.34% 61.18% 30.51% 7.10% 25.48% 54.05%
## 929: 19.34% 61.18% 30.51% 7.18% 25.34% 54.05%
## 930: 19.30% 61.18% 30.30% 7.10% 25.48% 54.05%
## 931: 19.26% 61.18% 30.30% 7.10% 25.34% 54.05%
## 932: 19.22% 61.18% 30.30% 7.10% 25.19% 54.05%
## 933: 19.22% 61.18% 30.30% 7.10% 25.19% 54.05%
## 934: 19.26% 61.18% 30.30% 7.10% 25.34% 54.05%
## 935: 19.34% 62.35% 30.72% 7.02% 25.34% 54.05%
## 936: 19.34% 62.35% 30.72% 7.02% 25.34% 54.05%
## 937: 19.30% 62.35% 30.72% 6.94% 25.34% 54.05%
## 938: 19.30% 62.35% 30.30% 7.10% 25.34% 54.05%
## 939: 19.30% 62.35% 30.30% 7.10% 25.34% 54.05%
## 940: 19.26% 62.35% 30.30% 7.02% 25.34% 54.05%
## 941: 19.26% 62.35% 30.30% 7.02% 25.34% 54.05%
## 942: 19.26% 62.35% 30.30% 7.02% 25.34% 54.05%
## 943: 19.34% 62.35% 30.51% 7.02% 25.48% 54.05%
## 944: 19.34% 62.35% 30.51% 6.94% 25.63% 54.05%
## 945: 19.30% 62.35% 30.30% 7.02% 25.48% 54.05%
## 946: 19.30% 62.35% 30.51% 6.94% 25.48% 54.05%
## 947: 19.30% 62.35% 30.30% 7.02% 25.48% 54.05%
## 948: 19.26% 62.35% 30.08% 7.02% 25.48% 54.05%
## 949: 19.30% 62.35% 30.30% 7.02% 25.48% 54.05%
## 950: 19.26% 62.35% 30.30% 6.94% 25.63% 52.70%
## 951: 19.22% 62.35% 30.08% 7.02% 25.34% 54.05%
## 952: 19.22% 62.35% 30.08% 7.02% 25.34% 54.05%
## 953: 19.22% 62.35% 30.08% 7.02% 25.34% 54.05%
## 954: 19.30% 62.35% 30.08% 7.02% 25.48% 55.41%
## 955: 19.26% 62.35% 30.30% 7.02% 25.34% 54.05%
## 956: 19.38% 62.35% 30.30% 7.10% 25.48% 55.41%
## 957: 19.38% 62.35% 30.08% 7.18% 25.48% 55.41%
## 958: 19.30% 61.18% 30.08% 7.10% 25.48% 55.41%
## 959: 19.34% 62.35% 30.08% 7.10% 25.48% 55.41%
## 960: 19.38% 62.35% 30.08% 7.18% 25.48% 55.41%
## 961: 19.34% 61.18% 30.08% 7.18% 25.48% 55.41%
## 962: 19.42% 62.35% 30.30% 7.10% 25.63% 55.41%
## 963: 19.38% 61.18% 30.51% 7.10% 25.63% 54.05%
## 964: 19.46% 62.35% 30.51% 7.10% 25.63% 55.41%
## 965: 19.26% 61.18% 30.51% 6.94% 25.48% 54.05%
## 966: 19.30% 62.35% 30.51% 6.94% 25.48% 54.05%
## 967: 19.34% 62.35% 30.51% 6.94% 25.48% 55.41%
## 968: 19.26% 61.18% 30.51% 6.94% 25.34% 55.41%
## 969: 19.26% 60.00% 30.51% 6.94% 25.48% 55.41%
## 970: 19.30% 61.18% 30.51% 6.94% 25.48% 55.41%
## 971: 19.22% 61.18% 30.30% 6.94% 25.34% 55.41%
## 972: 19.26% 61.18% 30.51% 6.94% 25.34% 55.41%
## 973: 19.26% 62.35% 30.30% 6.94% 25.34% 55.41%
## 974: 19.22% 61.18% 30.30% 6.94% 25.34% 55.41%
## 975: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 976: 19.30% 61.18% 30.30% 7.02% 25.48% 55.41%
## 977: 19.26% 61.18% 30.30% 7.02% 25.34% 55.41%
## 978: 19.22% 61.18% 30.30% 6.94% 25.34% 55.41%
## 979: 19.22% 61.18% 30.30% 6.94% 25.34% 55.41%
## 980: 19.22% 61.18% 30.30% 6.94% 25.34% 55.41%
## 981: 19.30% 61.18% 30.30% 7.02% 25.48% 55.41%
## 982: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 983: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 984: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 985: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 986: 19.26% 61.18% 30.51% 6.94% 25.34% 55.41%
## 987: 19.18% 61.18% 30.30% 6.86% 25.34% 55.41%
## 988: 19.18% 61.18% 30.30% 6.94% 25.19% 55.41%
## 989: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 990: 19.30% 61.18% 30.30% 7.02% 25.48% 55.41%
## 991: 19.18% 61.18% 30.30% 6.94% 25.19% 55.41%
## 992: 19.30% 61.18% 30.51% 6.94% 25.48% 55.41%
## 993: 19.26% 61.18% 30.51% 6.94% 25.34% 55.41%
## 994: 19.18% 61.18% 30.30% 6.78% 25.48% 55.41%
## 995: 19.26% 61.18% 30.51% 6.86% 25.48% 55.41%
## 996: 19.22% 61.18% 30.30% 6.86% 25.48% 55.41%
## 997: 19.18% 61.18% 30.30% 6.78% 25.48% 55.41%
## 998: 19.14% 61.18% 30.30% 6.78% 25.34% 55.41%
## 999: 19.14% 61.18% 30.30% 6.78% 25.34% 55.41%
## 1000: 19.14% 61.18% 30.30% 6.78% 25.34% 55.41%
test2 <- predict(model_rf2, newdata = test)
table(test2, test$Hospital.overall.rating)
##
## test2 1 2 3 4 5
## 1 13 6 0 0 0
## 2 19 136 16 0 0
## 3 0 70 464 61 0
## 4 0 0 24 232 23
## 5 0 0 0 0 14
summary(model_rf2)
## Length Class Mode
## call 8 -none- call
## type 1 -none- character
## predicted 2570 factor numeric
## err.rate 6000 -none- numeric
## confusion 30 -none- numeric
## votes 12850 matrix numeric
## oob.times 2570 -none- numeric
## classes 5 -none- character
## importance 53 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 2570 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms call
conf_matrix2 <- confusionMatrix(test2, test$Hospital.overall.rating, positive = "Yes")
conf_matrix2
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 13 6 0 0 0
## 2 19 136 16 0 0
## 3 0 70 464 61 0
## 4 0 0 24 232 23
## 5 0 0 0 0 14
##
## Overall Statistics
##
## Accuracy : 0.7968
## 95% CI : (0.7716, 0.8205)
## No Information Rate : 0.4675
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6823
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.40625 0.6415 0.9206 0.7918 0.37838
## Specificity 0.99426 0.9596 0.7718 0.9401 1.00000
## Pos Pred Value 0.68421 0.7953 0.7798 0.8315 1.00000
## Neg Pred Value 0.98206 0.9162 0.9172 0.9237 0.97838
## Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
## Detection Rate 0.01206 0.1262 0.4304 0.2152 0.01299
## Detection Prevalence 0.01763 0.1586 0.5519 0.2588 0.01299
## Balanced Accuracy 0.70026 0.8005 0.8462 0.8660 0.68919
# Confusion Matrix and Statistics
#
# Reference
# Prediction 1 2 3 4 5
# 1 13 6 0 0 0
# 2 19 136 16 0 0
# 3 0 70 464 61 0
# 4 0 0 24 232 23
# 5 0 0 0 0 14
#
# Overall Statistics
#
# Accuracy : 0.7968
# 95% CI : (0.7716, 0.8205)
# No Information Rate : 0.4675
# P-Value [Acc > NIR] : < 2.2e-16
#
# Kappa : 0.6823
# Mcnemar's Test P-Value : NA
#
# Statistics by Class:
#
# Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
# Sensitivity 0.40625 0.6415 0.9206 0.7918 0.37838
# Specificity 0.99426 0.9596 0.7718 0.9401 1.00000
# Pos Pred Value 0.68421 0.7953 0.7798 0.8315 1.00000
# Neg Pred Value 0.98206 0.9162 0.9172 0.9237 0.97838
# Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
# Detection Rate 0.01206 0.1262 0.4304 0.2152 0.01299
# Detection Prevalence 0.01763 0.1586 0.5519 0.2588 0.01299
# Balanced Accuracy 0.70026 0.8005 0.8462 0.8660 0.68919
# Accuracy is 79.7%
model_rf3 <- randomForest(Hospital.overall.rating ~ ., data = train, promiximity = FALSE, ntree = 1500, mtry = 20, do.trace = TRUE, na.action = na.omit)
## ntree OOB 1 2 3 4 5
## 1: 40.35% 62.07% 50.00% 34.98% 38.36% 68.97%
## 2: 41.89% 54.90% 51.03% 37.05% 42.02% 52.63%
## 3: 39.64% 45.59% 49.06% 35.18% 38.74% 54.72%
## 4: 37.63% 41.33% 45.54% 33.21% 37.41% 59.32%
## 5: 36.82% 46.84% 45.73% 31.62% 36.91% 56.06%
## 6: 36.08% 53.57% 43.95% 29.77% 37.46% 58.33%
## 7: 35.02% 51.19% 45.18% 28.17% 36.26% 55.56%
## 8: 35.05% 42.86% 46.44% 27.28% 37.67% 60.81%
## 9: 33.84% 47.62% 44.54% 25.67% 37.27% 56.76%
## 10: 33.95% 48.24% 45.53% 25.42% 37.25% 58.11%
## 11: 32.73% 49.41% 42.89% 23.87% 37.65% 55.41%
## 12: 31.55% 44.71% 43.10% 22.19% 36.94% 54.05%
## 13: 30.87% 45.88% 42.04% 21.33% 36.42% 55.41%
## 14: 30.70% 43.53% 42.80% 21.72% 35.07% 52.70%
## 15: 29.79% 47.06% 40.47% 20.99% 34.18% 52.70%
## 16: 29.77% 47.06% 39.62% 21.06% 34.58% 52.70%
## 17: 28.05% 45.88% 36.86% 19.79% 32.49% 52.70%
## 18: 27.94% 45.88% 37.92% 19.01% 32.49% 55.41%
## 19: 28.13% 48.24% 37.92% 18.93% 32.94% 56.76%
## 20: 27.74% 49.41% 38.35% 17.90% 32.79% 58.11%
## 21: 27.16% 47.06% 37.71% 18.22% 31.00% 55.41%
## 22: 26.81% 47.06% 37.92% 17.19% 31.45% 55.41%
## 23: 26.34% 49.41% 37.08% 16.48% 31.59% 52.70%
## 24: 26.50% 51.76% 37.29% 17.03% 30.85% 51.35%
## 25: 26.46% 55.29% 37.08% 16.09% 31.45% 58.11%
## 26: 25.60% 51.76% 34.96% 16.48% 29.66% 55.41%
## 27: 25.72% 49.41% 36.65% 15.69% 30.85% 54.05%
## 28: 24.63% 48.24% 35.59% 14.67% 29.81% 51.35%
## 29: 24.47% 49.41% 32.63% 14.91% 30.25% 55.41%
## 30: 24.12% 50.59% 34.11% 13.64% 30.10% 55.41%
## 31: 24.16% 49.41% 35.59% 13.88% 29.21% 52.70%
## 32: 23.85% 51.76% 32.20% 13.88% 29.81% 55.41%
## 33: 24.09% 52.94% 32.84% 13.96% 29.51% 59.46%
## 34: 23.85% 49.41% 33.05% 13.56% 29.81% 58.11%
## 35: 23.70% 51.76% 33.05% 13.64% 28.91% 56.76%
## 36: 23.07% 52.94% 32.20% 12.07% 29.96% 56.76%
## 37: 23.07% 52.94% 33.05% 12.07% 29.36% 56.76%
## 38: 23.04% 54.12% 31.78% 12.07% 29.66% 59.46%
## 39: 22.72% 51.76% 31.99% 12.07% 28.91% 56.76%
## 40: 22.96% 51.76% 33.26% 12.30% 28.46% 56.76%
## 41: 22.45% 51.76% 32.20% 11.75% 28.46% 55.41%
## 42: 22.80% 54.12% 32.20% 12.30% 28.32% 56.76%
## 43: 22.65% 51.76% 33.05% 11.99% 28.02% 56.76%
## 44: 22.65% 50.59% 32.20% 12.70% 27.57% 55.41%
## 45: 22.76% 51.76% 31.99% 12.85% 27.72% 55.41%
## 46: 22.45% 52.94% 31.57% 12.07% 28.17% 55.41%
## 47: 22.30% 52.94% 31.78% 11.99% 27.42% 56.76%
## 48: 21.95% 52.94% 30.08% 11.67% 28.02% 55.41%
## 49: 22.06% 54.12% 30.93% 11.44% 27.87% 58.11%
## 50: 22.26% 54.12% 32.42% 11.28% 27.87% 58.11%
## 51: 22.18% 54.12% 32.63% 11.28% 27.87% 54.05%
## 52: 22.10% 54.12% 33.47% 11.12% 27.27% 54.05%
## 53: 22.49% 55.29% 33.26% 11.51% 27.87% 55.41%
## 54: 22.30% 54.12% 32.63% 11.83% 27.12% 55.41%
## 55: 21.87% 55.29% 32.20% 11.20% 26.97% 54.05%
## 56: 22.06% 57.65% 33.05% 11.04% 26.97% 55.41%
## 57: 22.02% 57.65% 31.57% 11.12% 27.72% 55.41%
## 58: 21.83% 55.29% 31.78% 11.04% 27.27% 55.41%
## 59: 21.63% 51.76% 32.20% 10.96% 26.83% 55.41%
## 60: 21.75% 56.47% 31.99% 10.73% 27.27% 55.41%
## 61: 21.83% 52.94% 32.63% 10.96% 27.12% 55.41%
## 62: 21.56% 54.12% 32.63% 10.65% 26.53% 55.41%
## 63: 21.83% 55.29% 32.84% 11.04% 26.53% 55.41%
## 64: 21.83% 52.94% 33.05% 10.73% 27.27% 55.41%
## 65: 21.91% 52.94% 34.53% 10.65% 26.83% 54.05%
## 66: 21.71% 52.94% 33.90% 10.41% 26.83% 55.41%
## 67: 21.91% 55.29% 34.75% 10.49% 26.68% 54.05%
## 68: 21.79% 52.94% 34.11% 10.73% 26.53% 54.05%
## 69: 21.67% 52.94% 33.47% 10.65% 26.53% 55.41%
## 70: 21.60% 48.24% 33.47% 10.41% 27.27% 55.41%
## 71: 21.67% 50.59% 33.26% 10.96% 26.38% 55.41%
## 72: 21.98% 54.12% 33.26% 10.88% 27.27% 55.41%
## 73: 21.87% 52.94% 33.05% 10.73% 27.42% 55.41%
## 74: 21.91% 51.76% 33.26% 10.96% 27.27% 54.05%
## 75: 22.10% 52.94% 34.32% 10.65% 27.57% 55.41%
## 76: 21.91% 54.12% 33.26% 10.73% 27.27% 55.41%
## 77: 21.32% 52.94% 33.26% 10.09% 26.53% 54.05%
## 78: 21.48% 55.29% 33.47% 10.17% 26.38% 55.41%
## 79: 21.40% 56.47% 32.42% 10.17% 26.83% 54.05%
## 80: 21.36% 55.29% 32.42% 9.94% 27.27% 54.05%
## 81: 21.44% 60.00% 32.63% 9.94% 26.97% 52.70%
## 82: 21.56% 58.82% 33.05% 9.94% 27.12% 54.05%
## 83: 21.28% 56.47% 32.84% 9.54% 27.27% 54.05%
## 84: 21.48% 56.47% 33.26% 9.86% 27.12% 54.05%
## 85: 21.13% 57.65% 32.20% 9.54% 26.97% 54.05%
## 86: 21.05% 56.47% 31.78% 9.38% 27.42% 54.05%
## 87: 21.09% 58.82% 31.99% 9.31% 27.57% 51.35%
## 88: 21.17% 56.47% 32.63% 9.38% 27.42% 52.70%
## 89: 21.05% 54.12% 31.57% 9.54% 27.87% 51.35%
## 90: 21.09% 54.12% 32.63% 9.23% 28.02% 50.00%
## 91: 20.93% 56.47% 31.78% 9.23% 27.72% 50.00%
## 92: 21.32% 56.47% 32.42% 9.62% 28.02% 50.00%
## 93: 21.05% 57.65% 31.99% 9.46% 27.42% 50.00%
## 94: 20.89% 57.65% 31.78% 9.31% 27.27% 50.00%
## 95: 21.09% 57.65% 31.78% 9.62% 27.27% 51.35%
## 96: 20.86% 57.65% 32.42% 9.31% 26.68% 50.00%
## 97: 20.86% 55.29% 33.05% 9.15% 26.68% 51.35%
## 98: 21.09% 57.65% 32.42% 9.62% 26.83% 51.35%
## 99: 20.74% 56.47% 32.20% 8.99% 26.97% 51.35%
## 100: 20.89% 58.82% 31.99% 9.23% 26.97% 51.35%
## 101: 20.82% 58.82% 32.42% 9.15% 26.53% 51.35%
## 102: 20.74% 56.47% 31.57% 9.46% 26.53% 51.35%
## 103: 20.51% 56.47% 31.57% 8.75% 26.97% 51.35%
## 104: 20.58% 57.65% 31.36% 9.07% 26.53% 52.70%
## 105: 20.78% 57.65% 31.57% 9.15% 26.97% 52.70%
## 106: 20.43% 56.47% 31.36% 8.68% 26.83% 52.70%
## 107: 20.78% 57.65% 31.57% 9.07% 27.12% 52.70%
## 108: 20.74% 57.65% 31.36% 8.83% 27.57% 52.70%
## 109: 20.97% 60.00% 31.78% 9.23% 27.12% 52.70%
## 110: 20.62% 57.65% 30.93% 9.23% 26.68% 52.70%
## 111: 20.82% 56.47% 31.78% 9.38% 26.83% 51.35%
## 112: 20.74% 58.82% 31.78% 8.99% 26.97% 51.35%
## 113: 20.97% 57.65% 31.36% 9.54% 27.27% 51.35%
## 114: 20.89% 57.65% 31.36% 9.31% 27.42% 51.35%
## 115: 20.66% 57.65% 30.72% 9.31% 26.97% 51.35%
## 116: 20.86% 57.65% 31.14% 9.15% 27.72% 51.35%
## 117: 20.89% 58.82% 31.14% 9.38% 27.27% 51.35%
## 118: 20.54% 58.82% 31.14% 8.91% 26.68% 52.70%
## 119: 20.66% 57.65% 30.72% 9.23% 26.97% 52.70%
## 120: 20.19% 58.82% 29.87% 8.75% 26.53% 52.70%
## 121: 20.31% 57.65% 30.30% 8.75% 26.83% 52.70%
## 122: 20.31% 57.65% 30.72% 8.75% 26.38% 54.05%
## 123: 20.31% 57.65% 29.87% 8.91% 26.68% 54.05%
## 124: 20.31% 55.29% 30.93% 8.99% 26.08% 54.05%
## 125: 20.23% 55.29% 30.30% 8.91% 26.53% 52.70%
## 126: 20.04% 55.29% 30.51% 8.60% 26.23% 52.70%
## 127: 20.23% 56.47% 30.30% 8.83% 26.53% 52.70%
## 128: 20.58% 56.47% 31.36% 8.68% 27.27% 54.05%
## 129: 20.27% 56.47% 31.14% 8.60% 26.53% 52.70%
## 130: 20.27% 55.29% 30.51% 8.68% 26.83% 54.05%
## 131: 20.16% 55.29% 31.14% 8.60% 26.23% 52.70%
## 132: 20.16% 56.47% 31.36% 8.52% 25.93% 54.05%
## 133: 20.23% 58.82% 30.93% 8.52% 26.23% 54.05%
## 134: 20.27% 57.65% 30.72% 8.68% 26.68% 51.35%
## 135: 20.47% 56.47% 31.57% 8.83% 26.38% 54.05%
## 136: 20.39% 57.65% 31.57% 8.52% 26.68% 52.70%
## 137: 20.39% 56.47% 31.36% 8.91% 26.38% 51.35%
## 138: 20.08% 57.65% 30.51% 8.52% 26.08% 54.05%
## 139: 20.27% 60.00% 31.14% 8.44% 26.23% 54.05%
## 140: 20.39% 58.82% 31.36% 8.28% 26.97% 54.05%
## 141: 20.47% 60.00% 31.14% 8.60% 26.83% 52.70%
## 142: 20.23% 57.65% 31.36% 8.36% 26.38% 54.05%
## 143: 20.43% 57.65% 31.14% 8.75% 26.53% 54.05%
## 144: 20.23% 57.65% 31.14% 8.44% 26.53% 52.70%
## 145: 20.43% 57.65% 31.57% 8.52% 26.68% 54.05%
## 146: 20.58% 57.65% 31.36% 8.60% 27.27% 54.05%
## 147: 20.58% 57.65% 31.36% 8.52% 27.57% 52.70%
## 148: 20.16% 57.65% 31.78% 8.12% 26.38% 52.70%
## 149: 20.19% 57.65% 31.14% 8.12% 26.83% 54.05%
## 150: 20.31% 57.65% 30.72% 8.20% 27.42% 54.05%
## 151: 20.51% 57.65% 31.57% 8.20% 27.57% 54.05%
## 152: 20.39% 57.65% 31.78% 8.04% 27.42% 52.70%
## 153: 20.23% 57.65% 30.72% 8.36% 26.97% 52.70%
## 154: 20.27% 57.65% 31.14% 8.12% 27.27% 52.70%
## 155: 20.19% 57.65% 31.14% 8.36% 26.53% 52.70%
## 156: 20.39% 57.65% 31.57% 8.68% 26.38% 52.70%
## 157: 20.19% 57.65% 30.93% 8.60% 26.23% 52.70%
## 158: 20.31% 57.65% 30.93% 8.52% 26.83% 52.70%
## 159: 20.27% 57.65% 31.14% 8.44% 26.53% 54.05%
## 160: 20.27% 57.65% 31.14% 8.36% 26.83% 52.70%
## 161: 20.35% 57.65% 31.14% 8.52% 26.83% 52.70%
## 162: 20.58% 58.82% 31.57% 8.44% 27.27% 54.05%
## 163: 20.54% 56.47% 31.99% 8.44% 27.12% 54.05%
## 164: 20.43% 57.65% 32.20% 8.12% 26.97% 54.05%
## 165: 20.31% 57.65% 31.78% 8.28% 26.53% 54.05%
## 166: 20.47% 57.65% 32.20% 8.44% 26.53% 54.05%
## 167: 20.47% 57.65% 31.99% 8.52% 26.53% 54.05%
## 168: 20.66% 57.65% 31.78% 8.68% 27.12% 54.05%
## 169: 20.54% 57.65% 31.57% 8.68% 26.83% 54.05%
## 170: 20.35% 58.82% 31.14% 8.44% 26.68% 54.05%
## 171: 20.51% 57.65% 30.72% 8.60% 27.57% 52.70%
## 172: 20.27% 57.65% 31.36% 8.28% 26.83% 52.70%
## 173: 20.51% 57.65% 30.93% 8.68% 27.27% 52.70%
## 174: 20.43% 57.65% 30.72% 8.75% 26.97% 52.70%
## 175: 20.62% 57.65% 31.36% 8.44% 27.87% 52.70%
## 176: 20.39% 57.65% 31.14% 8.36% 27.27% 52.70%
## 177: 20.47% 57.65% 31.36% 8.60% 26.97% 52.70%
## 178: 20.31% 57.65% 31.36% 8.44% 26.68% 52.70%
## 179: 20.16% 57.65% 31.36% 8.20% 26.53% 52.70%
## 180: 20.16% 57.65% 30.72% 8.36% 26.68% 52.70%
## 181: 19.96% 57.65% 29.87% 8.44% 26.38% 52.70%
## 182: 20.23% 58.82% 30.93% 8.52% 26.38% 52.70%
## 183: 20.27% 57.65% 30.51% 8.75% 26.38% 54.05%
## 184: 20.51% 58.82% 30.72% 8.68% 27.12% 54.05%
## 185: 20.08% 58.82% 29.66% 8.52% 26.68% 52.70%
## 186: 20.16% 58.82% 30.30% 8.60% 26.38% 52.70%
## 187: 20.12% 58.82% 30.72% 8.36% 26.38% 52.70%
## 188: 20.16% 58.82% 30.72% 8.36% 26.53% 52.70%
## 189: 20.31% 58.82% 30.51% 8.52% 26.83% 54.05%
## 190: 20.04% 58.82% 29.66% 8.44% 26.68% 52.70%
## 191: 20.16% 58.82% 31.14% 8.28% 26.53% 51.35%
## 192: 19.96% 58.82% 30.30% 8.12% 26.53% 52.70%
## 193: 20.12% 58.82% 30.72% 8.36% 26.38% 52.70%
## 194: 20.00% 58.82% 30.30% 8.12% 26.53% 54.05%
## 195: 19.96% 58.82% 30.08% 8.44% 26.08% 52.70%
## 196: 20.00% 58.82% 30.51% 8.36% 26.08% 52.70%
## 197: 20.16% 58.82% 30.72% 8.36% 26.53% 52.70%
## 198: 20.12% 58.82% 30.30% 8.36% 26.68% 52.70%
## 199: 19.96% 58.82% 29.87% 8.44% 26.23% 52.70%
## 200: 19.92% 58.82% 30.93% 7.97% 26.23% 52.70%
## 201: 19.88% 58.82% 30.30% 8.12% 26.23% 52.70%
## 202: 19.92% 58.82% 30.08% 8.04% 26.68% 52.70%
## 203: 20.08% 58.82% 30.30% 8.36% 26.53% 52.70%
## 204: 20.00% 58.82% 29.87% 8.28% 26.68% 52.70%
## 205: 20.00% 58.82% 29.87% 8.20% 26.97% 51.35%
## 206: 19.84% 58.82% 29.66% 8.04% 26.53% 54.05%
## 207: 19.92% 58.82% 29.66% 8.28% 26.38% 54.05%
## 208: 20.04% 58.82% 30.08% 8.28% 26.53% 54.05%
## 209: 20.12% 58.82% 29.87% 8.52% 26.53% 54.05%
## 210: 19.88% 58.82% 29.24% 8.52% 26.23% 52.70%
## 211: 20.00% 58.82% 29.66% 8.44% 26.38% 54.05%
## 212: 19.96% 58.82% 29.87% 8.28% 26.53% 52.70%
## 213: 20.00% 58.82% 30.08% 8.12% 26.83% 52.70%
## 214: 20.00% 58.82% 29.87% 8.28% 26.68% 52.70%
## 215: 19.77% 58.82% 28.81% 8.12% 26.83% 52.70%
## 216: 19.96% 58.82% 29.03% 8.44% 26.83% 52.70%
## 217: 19.92% 58.82% 29.45% 8.04% 27.12% 52.70%
## 218: 20.04% 58.82% 29.87% 8.36% 26.53% 54.05%
## 219: 19.88% 58.82% 29.87% 8.20% 26.38% 52.70%
## 220: 19.96% 58.82% 29.66% 8.36% 26.53% 52.70%
## 221: 20.12% 58.82% 30.30% 8.28% 26.68% 54.05%
## 222: 19.88% 58.82% 29.66% 8.12% 26.53% 54.05%
## 223: 20.16% 58.82% 30.08% 8.44% 26.68% 54.05%
## 224: 19.96% 58.82% 30.51% 8.04% 26.53% 52.70%
## 225: 20.08% 58.82% 30.51% 8.12% 26.97% 51.35%
## 226: 19.92% 58.82% 30.30% 8.04% 26.68% 51.35%
## 227: 19.88% 58.82% 30.30% 7.97% 26.68% 51.35%
## 228: 20.08% 58.82% 30.30% 8.20% 26.97% 51.35%
## 229: 19.96% 58.82% 29.66% 8.12% 27.12% 51.35%
## 230: 19.92% 58.82% 30.08% 8.20% 26.53% 51.35%
## 231: 20.00% 58.82% 30.08% 8.12% 26.97% 51.35%
## 232: 19.73% 58.82% 29.87% 7.73% 26.83% 51.35%
## 233: 19.84% 57.65% 29.66% 8.12% 26.83% 51.35%
## 234: 19.84% 58.82% 29.66% 7.97% 26.97% 51.35%
## 235: 19.84% 57.65% 29.87% 8.04% 26.83% 51.35%
## 236: 19.81% 58.82% 29.87% 7.89% 26.83% 51.35%
## 237: 19.77% 58.82% 29.45% 7.81% 26.97% 52.70%
## 238: 19.77% 58.82% 29.87% 7.81% 26.83% 51.35%
## 239: 19.69% 58.82% 29.45% 7.81% 26.83% 51.35%
## 240: 19.88% 57.65% 29.66% 8.20% 26.83% 51.35%
## 241: 19.81% 58.82% 29.45% 8.04% 26.83% 51.35%
## 242: 19.81% 58.82% 29.24% 8.04% 26.83% 52.70%
## 243: 19.81% 58.82% 29.45% 8.04% 26.68% 52.70%
## 244: 20.12% 58.82% 30.08% 8.44% 26.83% 51.35%
## 245: 20.08% 57.65% 30.30% 8.20% 26.97% 52.70%
## 246: 19.73% 57.65% 29.87% 7.89% 26.53% 52.70%
## 247: 19.81% 58.82% 29.87% 7.89% 26.68% 52.70%
## 248: 19.88% 58.82% 30.08% 7.57% 27.42% 52.70%
## 249: 19.81% 57.65% 29.45% 7.81% 27.27% 52.70%
## 250: 20.08% 57.65% 29.87% 8.28% 27.12% 52.70%
## 251: 19.88% 57.65% 30.30% 7.89% 26.83% 52.70%
## 252: 20.19% 57.65% 30.72% 8.12% 27.27% 52.70%
## 253: 20.08% 57.65% 30.30% 8.04% 27.27% 52.70%
## 254: 19.92% 57.65% 30.51% 8.04% 26.53% 52.70%
## 255: 20.16% 57.65% 31.14% 8.12% 26.83% 52.70%
## 256: 20.12% 57.65% 30.93% 8.20% 26.68% 52.70%
## 257: 20.04% 57.65% 30.93% 8.04% 26.68% 52.70%
## 258: 20.08% 57.65% 30.93% 8.12% 26.68% 52.70%
## 259: 20.00% 58.82% 30.93% 8.04% 26.38% 52.70%
## 260: 20.12% 58.82% 31.14% 8.04% 26.68% 52.70%
## 261: 20.12% 57.65% 31.14% 8.04% 26.83% 52.70%
## 262: 20.00% 57.65% 31.14% 7.89% 26.68% 52.70%
## 263: 20.00% 58.82% 31.36% 7.97% 26.23% 52.70%
## 264: 20.00% 58.82% 31.36% 7.89% 26.38% 52.70%
## 265: 20.16% 58.82% 31.78% 8.12% 26.23% 52.70%
## 266: 19.96% 60.00% 30.72% 8.12% 26.08% 52.70%
## 267: 20.04% 60.00% 31.57% 7.97% 26.08% 52.70%
## 268: 19.96% 60.00% 31.57% 7.81% 26.08% 52.70%
## 269: 19.92% 60.00% 30.93% 7.81% 26.38% 52.70%
## 270: 19.92% 60.00% 30.93% 7.89% 26.23% 52.70%
## 271: 19.65% 58.82% 30.30% 7.73% 26.08% 52.70%
## 272: 19.81% 58.82% 30.72% 7.89% 26.08% 52.70%
## 273: 19.69% 58.82% 30.51% 7.81% 25.93% 52.70%
## 274: 19.61% 58.82% 30.30% 7.73% 25.93% 52.70%
## 275: 19.77% 57.65% 30.51% 7.97% 26.08% 52.70%
## 276: 19.88% 58.82% 31.36% 7.65% 26.38% 52.70%
## 277: 20.08% 60.00% 32.20% 7.73% 26.23% 52.70%
## 278: 20.04% 60.00% 31.99% 7.73% 26.23% 52.70%
## 279: 20.27% 60.00% 32.20% 7.97% 26.53% 52.70%
## 280: 20.31% 60.00% 31.78% 8.12% 26.68% 52.70%
## 281: 20.16% 60.00% 31.57% 8.04% 26.38% 52.70%
## 282: 19.88% 60.00% 31.36% 7.65% 26.23% 52.70%
## 283: 19.96% 60.00% 31.36% 7.81% 26.38% 51.35%
## 284: 19.84% 60.00% 30.93% 7.89% 25.93% 52.70%
## 285: 19.96% 60.00% 31.36% 7.81% 26.23% 52.70%
## 286: 20.00% 60.00% 31.57% 7.73% 26.38% 52.70%
## 287: 19.92% 60.00% 31.36% 7.73% 26.23% 52.70%
## 288: 20.12% 61.18% 31.57% 7.97% 26.23% 52.70%
## 289: 20.16% 60.00% 31.57% 8.12% 26.23% 52.70%
## 290: 20.04% 60.00% 31.36% 7.89% 26.38% 52.70%
## 291: 20.00% 61.18% 31.14% 7.81% 26.38% 52.70%
## 292: 19.88% 60.00% 31.36% 7.81% 25.93% 52.70%
## 293: 19.88% 61.18% 31.14% 7.97% 25.63% 52.70%
## 294: 19.92% 61.18% 31.36% 7.81% 25.93% 52.70%
## 295: 20.08% 61.18% 31.36% 7.97% 26.23% 52.70%
## 296: 19.96% 61.18% 30.93% 8.04% 25.93% 52.70%
## 297: 19.96% 61.18% 31.14% 8.04% 25.78% 52.70%
## 298: 19.92% 61.18% 30.93% 8.04% 25.78% 52.70%
## 299: 20.00% 61.18% 31.14% 8.12% 25.78% 52.70%
## 300: 19.92% 61.18% 30.93% 8.12% 25.78% 51.35%
## 301: 19.84% 61.18% 31.36% 7.81% 25.78% 51.35%
## 302: 19.73% 61.18% 30.93% 7.89% 25.63% 50.00%
## 303: 20.00% 61.18% 30.93% 7.97% 26.38% 51.35%
## 304: 19.84% 61.18% 30.93% 7.89% 25.93% 51.35%
## 305: 19.88% 61.18% 30.72% 7.89% 26.23% 51.35%
## 306: 19.92% 61.18% 30.93% 7.97% 26.08% 51.35%
## 307: 19.96% 61.18% 31.14% 7.89% 26.23% 51.35%
## 308: 19.84% 61.18% 30.93% 7.81% 26.08% 51.35%
## 309: 20.00% 61.18% 30.93% 8.04% 26.23% 51.35%
## 310: 20.12% 61.18% 31.14% 8.12% 26.38% 51.35%
## 311: 20.04% 61.18% 31.36% 7.97% 26.23% 51.35%
## 312: 20.12% 61.18% 31.78% 7.89% 26.38% 51.35%
## 313: 19.84% 61.18% 31.36% 7.81% 25.78% 51.35%
## 314: 19.84% 61.18% 31.14% 7.81% 25.93% 51.35%
## 315: 19.84% 61.18% 31.57% 7.81% 25.78% 50.00%
## 316: 19.92% 61.18% 31.57% 7.81% 26.08% 50.00%
## 317: 19.92% 61.18% 31.57% 7.73% 26.23% 50.00%
## 318: 19.92% 61.18% 31.78% 7.89% 25.78% 50.00%
## 319: 19.84% 61.18% 31.78% 7.65% 25.93% 50.00%
## 320: 19.84% 61.18% 31.57% 7.81% 25.78% 50.00%
## 321: 19.73% 61.18% 31.57% 7.73% 25.48% 50.00%
## 322: 19.65% 61.18% 31.36% 7.65% 25.48% 50.00%
## 323: 19.69% 61.18% 31.57% 7.65% 25.48% 50.00%
## 324: 19.88% 61.18% 31.99% 7.65% 25.93% 50.00%
## 325: 19.81% 61.18% 31.78% 7.65% 25.78% 50.00%
## 326: 19.84% 61.18% 31.78% 7.65% 25.93% 50.00%
## 327: 19.77% 61.18% 31.99% 7.65% 25.48% 50.00%
## 328: 19.84% 61.18% 31.78% 7.73% 25.78% 50.00%
## 329: 19.73% 61.18% 31.36% 7.65% 25.78% 50.00%
## 330: 19.84% 61.18% 31.57% 7.73% 25.93% 50.00%
## 331: 19.69% 61.18% 31.57% 7.65% 25.48% 50.00%
## 332: 19.61% 61.18% 31.36% 7.65% 25.34% 50.00%
## 333: 19.53% 61.18% 31.36% 7.65% 25.04% 50.00%
## 334: 19.69% 61.18% 31.36% 7.65% 25.63% 50.00%
## 335: 19.81% 61.18% 31.36% 7.73% 25.93% 50.00%
## 336: 19.84% 62.35% 31.14% 7.65% 26.23% 50.00%
## 337: 19.73% 61.18% 31.14% 7.65% 25.93% 50.00%
## 338: 19.81% 61.18% 31.14% 7.81% 25.93% 50.00%
## 339: 19.77% 61.18% 31.14% 7.73% 25.93% 50.00%
## 340: 19.69% 61.18% 31.36% 7.73% 25.48% 50.00%
## 341: 19.84% 61.18% 31.36% 7.73% 26.08% 50.00%
## 342: 19.92% 62.35% 31.78% 7.57% 26.23% 50.00%
## 343: 19.84% 61.18% 31.36% 7.73% 26.08% 50.00%
## 344: 19.88% 61.18% 31.57% 7.81% 25.93% 50.00%
## 345: 19.81% 62.35% 31.14% 7.73% 25.93% 50.00%
## 346: 19.84% 62.35% 31.14% 7.81% 25.93% 50.00%
## 347: 19.92% 62.35% 31.57% 7.73% 26.08% 50.00%
## 348: 19.92% 62.35% 31.57% 7.73% 26.08% 50.00%
## 349: 20.00% 62.35% 31.78% 7.81% 26.08% 50.00%
## 350: 19.96% 62.35% 31.78% 7.89% 25.78% 50.00%
## 351: 19.96% 62.35% 31.57% 7.89% 25.93% 50.00%
## 352: 19.88% 62.35% 31.78% 7.65% 25.93% 50.00%
## 353: 19.96% 62.35% 32.20% 7.73% 25.78% 50.00%
## 354: 19.96% 62.35% 31.99% 7.65% 26.08% 50.00%
## 355: 20.00% 62.35% 31.78% 7.81% 26.08% 50.00%
## 356: 19.84% 62.35% 31.78% 7.65% 25.78% 50.00%
## 357: 19.73% 62.35% 31.78% 7.57% 25.48% 50.00%
## 358: 19.84% 62.35% 31.99% 7.65% 25.48% 51.35%
## 359: 19.84% 62.35% 31.99% 7.49% 25.78% 51.35%
## 360: 20.04% 63.53% 32.20% 7.65% 25.93% 51.35%
## 361: 20.00% 63.53% 31.99% 7.57% 26.08% 51.35%
## 362: 20.00% 62.35% 32.20% 7.49% 26.23% 51.35%
## 363: 20.08% 63.53% 32.42% 7.49% 26.08% 52.70%
## 364: 20.04% 63.53% 32.42% 7.49% 25.93% 52.70%
## 365: 20.19% 62.35% 32.84% 7.73% 25.93% 52.70%
## 366: 20.12% 63.53% 32.84% 7.57% 25.78% 52.70%
## 367: 20.04% 63.53% 32.63% 7.49% 25.78% 52.70%
## 368: 20.12% 63.53% 32.84% 7.57% 25.78% 52.70%
## 369: 20.12% 63.53% 32.63% 7.57% 25.93% 52.70%
## 370: 20.04% 63.53% 32.42% 7.49% 25.93% 52.70%
## 371: 20.12% 63.53% 32.20% 7.65% 26.08% 52.70%
## 372: 20.12% 63.53% 32.20% 7.65% 26.08% 52.70%
## 373: 20.08% 63.53% 31.99% 7.73% 25.93% 52.70%
## 374: 20.27% 63.53% 32.63% 7.73% 26.23% 52.70%
## 375: 20.19% 63.53% 32.20% 7.73% 26.23% 52.70%
## 376: 20.27% 63.53% 32.63% 7.73% 26.23% 52.70%
## 377: 20.23% 63.53% 32.20% 7.89% 26.08% 52.70%
## 378: 20.08% 63.53% 32.20% 7.73% 25.78% 52.70%
## 379: 20.00% 63.53% 31.78% 7.73% 25.78% 52.70%
## 380: 19.84% 62.35% 31.57% 7.65% 25.63% 52.70%
## 381: 19.84% 62.35% 31.78% 7.65% 25.48% 52.70%
## 382: 20.08% 62.35% 32.42% 7.89% 25.48% 52.70%
## 383: 20.00% 63.53% 32.20% 7.57% 25.78% 52.70%
## 384: 20.00% 63.53% 31.99% 7.73% 25.63% 52.70%
## 385: 19.84% 63.53% 31.78% 7.41% 25.78% 52.70%
## 386: 19.81% 63.53% 31.57% 7.33% 25.93% 52.70%
## 387: 19.88% 63.53% 31.99% 7.41% 25.78% 52.70%
## 388: 19.96% 63.53% 31.78% 7.49% 26.08% 52.70%
## 389: 19.88% 63.53% 31.78% 7.41% 25.93% 52.70%
## 390: 19.96% 63.53% 31.99% 7.41% 26.08% 52.70%
## 391: 19.96% 63.53% 31.78% 7.41% 26.23% 52.70%
## 392: 20.00% 63.53% 31.99% 7.49% 26.08% 52.70%
## 393: 19.96% 63.53% 31.57% 7.49% 26.23% 52.70%
## 394: 19.69% 62.35% 31.57% 7.41% 25.48% 52.70%
## 395: 19.65% 62.35% 31.36% 7.41% 25.48% 52.70%
## 396: 19.77% 63.53% 31.78% 7.41% 25.48% 52.70%
## 397: 19.77% 62.35% 31.78% 7.41% 25.63% 52.70%
## 398: 19.77% 62.35% 31.57% 7.41% 25.78% 52.70%
## 399: 19.73% 62.35% 31.36% 7.49% 25.63% 52.70%
## 400: 19.73% 62.35% 31.57% 7.41% 25.63% 52.70%
## 401: 19.65% 62.35% 31.14% 7.41% 25.63% 52.70%
## 402: 19.69% 62.35% 31.36% 7.49% 25.48% 52.70%
## 403: 19.81% 62.35% 31.78% 7.57% 25.48% 52.70%
## 404: 19.84% 62.35% 31.57% 7.57% 25.78% 52.70%
## 405: 19.73% 62.35% 31.36% 7.41% 25.78% 52.70%
## 406: 19.81% 62.35% 31.57% 7.41% 25.93% 52.70%
## 407: 19.92% 62.35% 31.78% 7.57% 25.93% 52.70%
## 408: 19.96% 63.53% 31.99% 7.49% 25.93% 52.70%
## 409: 19.92% 62.35% 31.57% 7.65% 25.93% 52.70%
## 410: 19.77% 62.35% 31.36% 7.49% 25.78% 52.70%
## 411: 20.04% 63.53% 31.78% 7.73% 25.93% 52.70%
## 412: 19.73% 63.53% 30.72% 7.57% 25.78% 52.70%
## 413: 19.73% 62.35% 31.14% 7.41% 25.93% 52.70%
## 414: 19.61% 63.53% 30.72% 7.49% 25.48% 52.70%
## 415: 19.77% 62.35% 31.57% 7.41% 25.78% 52.70%
## 416: 19.81% 63.53% 31.14% 7.57% 25.78% 52.70%
## 417: 19.73% 62.35% 30.72% 7.57% 25.93% 52.70%
## 418: 19.69% 62.35% 31.36% 7.33% 25.78% 52.70%
## 419: 19.69% 63.53% 30.93% 7.41% 25.78% 52.70%
## 420: 19.65% 63.53% 31.14% 7.33% 25.63% 52.70%
## 421: 19.73% 63.53% 31.36% 7.41% 25.63% 52.70%
## 422: 19.77% 63.53% 31.57% 7.18% 26.08% 52.70%
## 423: 19.65% 63.53% 31.14% 7.26% 25.78% 52.70%
## 424: 19.73% 63.53% 30.51% 7.49% 26.08% 52.70%
## 425: 19.73% 63.53% 30.93% 7.26% 26.23% 52.70%
## 426: 19.81% 63.53% 30.72% 7.41% 26.38% 52.70%
## 427: 19.92% 63.53% 31.14% 7.41% 26.53% 52.70%
## 428: 19.81% 63.53% 31.14% 7.33% 26.23% 52.70%
## 429: 19.77% 63.53% 30.72% 7.49% 26.08% 52.70%
## 430: 19.81% 63.53% 30.93% 7.49% 26.08% 52.70%
## 431: 19.77% 63.53% 30.93% 7.41% 26.08% 52.70%
## 432: 19.69% 63.53% 30.51% 7.49% 25.93% 52.70%
## 433: 19.73% 63.53% 30.93% 7.33% 26.08% 52.70%
## 434: 19.61% 63.53% 30.72% 7.26% 26.08% 51.35%
## 435: 19.84% 63.53% 31.14% 7.49% 26.08% 52.70%
## 436: 19.81% 63.53% 30.72% 7.49% 26.23% 52.70%
## 437: 19.69% 63.53% 30.51% 7.41% 26.08% 52.70%
## 438: 19.65% 63.53% 30.30% 7.41% 26.08% 52.70%
## 439: 19.61% 63.53% 30.30% 7.49% 25.78% 52.70%
## 440: 19.73% 63.53% 30.93% 7.49% 25.78% 52.70%
## 441: 19.73% 63.53% 30.93% 7.49% 25.78% 52.70%
## 442: 19.57% 63.53% 30.30% 7.33% 26.08% 51.35%
## 443: 19.57% 63.53% 30.30% 7.41% 25.78% 52.70%
## 444: 19.65% 63.53% 30.51% 7.41% 25.93% 52.70%
## 445: 19.73% 63.53% 31.14% 7.49% 25.63% 52.70%
## 446: 19.65% 63.53% 31.14% 7.18% 25.93% 52.70%
## 447: 19.61% 63.53% 30.93% 7.18% 25.93% 52.70%
## 448: 19.61% 63.53% 30.72% 7.26% 25.93% 52.70%
## 449: 19.77% 63.53% 31.36% 7.41% 25.78% 52.70%
## 450: 19.69% 63.53% 30.72% 7.26% 26.23% 52.70%
## 451: 19.77% 63.53% 30.93% 7.33% 26.23% 52.70%
## 452: 19.69% 63.53% 30.72% 7.33% 26.08% 52.70%
## 453: 19.73% 63.53% 31.14% 7.26% 26.08% 52.70%
## 454: 19.73% 63.53% 31.14% 7.41% 25.78% 52.70%
## 455: 19.65% 63.53% 31.36% 7.26% 25.63% 52.70%
## 456: 19.77% 63.53% 31.36% 7.33% 25.93% 52.70%
## 457: 19.69% 63.53% 31.36% 7.26% 25.78% 52.70%
## 458: 19.81% 63.53% 31.36% 7.33% 26.08% 52.70%
## 459: 19.88% 63.53% 31.36% 7.41% 26.23% 52.70%
## 460: 19.96% 63.53% 31.57% 7.49% 26.23% 52.70%
## 461: 19.88% 63.53% 31.57% 7.49% 25.93% 52.70%
## 462: 19.84% 63.53% 31.36% 7.49% 25.93% 52.70%
## 463: 19.77% 63.53% 30.93% 7.41% 26.08% 52.70%
## 464: 19.88% 63.53% 30.93% 7.81% 25.78% 52.70%
## 465: 19.65% 63.53% 30.51% 7.57% 25.63% 52.70%
## 466: 19.81% 63.53% 30.72% 7.65% 25.93% 52.70%
## 467: 19.84% 63.53% 30.93% 7.65% 25.93% 52.70%
## 468: 19.88% 63.53% 31.14% 7.65% 25.93% 52.70%
## 469: 19.77% 62.35% 31.57% 7.41% 25.78% 52.70%
## 470: 19.73% 62.35% 31.14% 7.41% 25.93% 52.70%
## 471: 19.77% 62.35% 30.93% 7.57% 25.93% 52.70%
## 472: 19.81% 62.35% 31.36% 7.57% 25.78% 52.70%
## 473: 19.77% 62.35% 31.57% 7.33% 25.93% 52.70%
## 474: 19.77% 62.35% 31.36% 7.41% 25.93% 52.70%
## 475: 19.77% 62.35% 31.14% 7.57% 25.78% 52.70%
## 476: 19.65% 62.35% 30.93% 7.57% 25.48% 52.70%
## 477: 19.77% 62.35% 31.14% 7.49% 25.93% 52.70%
## 478: 19.73% 62.35% 31.36% 7.41% 25.78% 52.70%
## 479: 19.69% 62.35% 31.36% 7.41% 25.63% 52.70%
## 480: 19.69% 62.35% 31.36% 7.49% 25.48% 52.70%
## 481: 19.77% 62.35% 31.36% 7.49% 25.78% 52.70%
## 482: 19.77% 62.35% 31.14% 7.49% 25.93% 52.70%
## 483: 19.69% 62.35% 31.14% 7.49% 25.63% 52.70%
## 484: 19.77% 62.35% 31.36% 7.49% 25.78% 52.70%
## 485: 19.81% 62.35% 31.36% 7.65% 25.63% 52.70%
## 486: 19.96% 62.35% 31.78% 7.65% 25.93% 52.70%
## 487: 19.96% 62.35% 31.57% 7.73% 25.93% 52.70%
## 488: 19.84% 62.35% 31.57% 7.49% 25.93% 52.70%
## 489: 19.88% 62.35% 31.36% 7.57% 26.08% 52.70%
## 490: 19.88% 62.35% 31.57% 7.49% 26.08% 52.70%
## 491: 19.96% 62.35% 31.78% 7.41% 26.38% 52.70%
## 492: 20.04% 62.35% 31.57% 7.49% 26.68% 52.70%
## 493: 20.00% 62.35% 31.57% 7.57% 26.38% 52.70%
## 494: 20.12% 62.35% 31.57% 7.65% 26.68% 52.70%
## 495: 19.92% 62.35% 31.36% 7.49% 26.38% 52.70%
## 496: 19.84% 62.35% 31.14% 7.57% 26.08% 52.70%
## 497: 19.77% 62.35% 31.14% 7.49% 25.93% 52.70%
## 498: 20.00% 62.35% 31.36% 7.73% 26.23% 52.70%
## 499: 19.96% 62.35% 31.57% 7.73% 25.93% 52.70%
## 500: 20.00% 62.35% 31.57% 7.73% 26.08% 52.70%
## 501: 19.92% 62.35% 31.36% 7.65% 26.08% 52.70%
## 502: 19.96% 62.35% 31.57% 7.49% 26.38% 52.70%
## 503: 19.96% 62.35% 31.57% 7.65% 26.08% 52.70%
## 504: 19.88% 62.35% 31.14% 7.65% 26.08% 52.70%
## 505: 19.96% 62.35% 31.36% 7.73% 26.08% 52.70%
## 506: 20.00% 62.35% 31.36% 7.65% 26.38% 52.70%
## 507: 19.92% 62.35% 31.36% 7.65% 26.08% 52.70%
## 508: 19.92% 61.18% 31.36% 7.73% 26.08% 52.70%
## 509: 20.00% 62.35% 31.36% 7.73% 26.23% 52.70%
## 510: 19.92% 62.35% 31.14% 7.73% 26.08% 52.70%
## 511: 19.88% 62.35% 31.14% 7.57% 26.23% 52.70%
## 512: 19.81% 61.18% 31.36% 7.57% 25.93% 52.70%
## 513: 19.61% 61.18% 30.93% 7.33% 25.93% 52.70%
## 514: 19.65% 61.18% 30.72% 7.49% 25.93% 52.70%
## 515: 19.69% 61.18% 31.14% 7.49% 25.78% 52.70%
## 516: 19.84% 61.18% 31.36% 7.57% 26.08% 52.70%
## 517: 19.73% 61.18% 31.36% 7.49% 25.78% 52.70%
## 518: 19.77% 61.18% 30.93% 7.57% 26.08% 52.70%
## 519: 19.84% 61.18% 30.93% 7.49% 26.53% 52.70%
## 520: 20.00% 61.18% 31.36% 7.73% 26.38% 52.70%
## 521: 19.88% 61.18% 30.93% 7.73% 26.23% 52.70%
## 522: 19.92% 61.18% 30.93% 7.89% 26.08% 52.70%
## 523: 19.96% 61.18% 30.93% 7.81% 26.38% 52.70%
## 524: 19.92% 61.18% 31.14% 7.81% 26.08% 52.70%
## 525: 19.81% 61.18% 30.93% 7.73% 25.93% 52.70%
## 526: 19.88% 61.18% 31.14% 7.65% 26.23% 52.70%
## 527: 19.92% 61.18% 31.36% 7.73% 26.08% 52.70%
## 528: 19.84% 61.18% 30.93% 7.65% 26.23% 52.70%
## 529: 19.81% 61.18% 31.36% 7.49% 26.08% 52.70%
## 530: 19.65% 61.18% 30.51% 7.49% 26.08% 52.70%
## 531: 19.77% 61.18% 30.72% 7.65% 26.08% 52.70%
## 532: 19.53% 61.18% 30.08% 7.41% 26.08% 52.70%
## 533: 19.61% 61.18% 30.30% 7.49% 26.08% 52.70%
## 534: 19.65% 61.18% 30.93% 7.41% 25.93% 52.70%
## 535: 19.69% 61.18% 30.93% 7.41% 26.08% 52.70%
## 536: 19.69% 61.18% 30.93% 7.26% 26.23% 54.05%
## 537: 19.61% 61.18% 30.51% 7.33% 26.23% 52.70%
## 538: 19.77% 61.18% 30.72% 7.57% 26.08% 54.05%
## 539: 19.73% 61.18% 30.93% 7.49% 25.93% 54.05%
## 540: 19.61% 61.18% 30.51% 7.41% 25.93% 54.05%
## 541: 19.69% 61.18% 30.93% 7.41% 25.93% 54.05%
## 542: 19.73% 61.18% 30.72% 7.57% 25.93% 54.05%
## 543: 19.77% 61.18% 31.14% 7.41% 26.08% 54.05%
## 544: 19.69% 61.18% 30.51% 7.41% 26.23% 54.05%
## 545: 19.65% 61.18% 30.72% 7.33% 26.08% 54.05%
## 546: 19.61% 61.18% 30.72% 7.33% 25.93% 54.05%
## 547: 19.65% 61.18% 30.72% 7.41% 25.93% 54.05%
## 548: 19.65% 61.18% 30.30% 7.57% 25.93% 54.05%
## 549: 19.49% 61.18% 30.72% 7.33% 25.48% 54.05%
## 550: 19.49% 61.18% 30.72% 7.33% 25.48% 54.05%
## 551: 19.61% 61.18% 30.72% 7.49% 25.63% 54.05%
## 552: 19.65% 61.18% 30.72% 7.57% 25.63% 54.05%
## 553: 19.77% 61.18% 30.93% 7.57% 25.93% 54.05%
## 554: 19.73% 61.18% 30.93% 7.57% 25.78% 54.05%
## 555: 19.73% 61.18% 30.72% 7.65% 25.78% 54.05%
## 556: 19.65% 61.18% 30.93% 7.65% 25.48% 52.70%
## 557: 19.61% 61.18% 30.93% 7.41% 25.63% 54.05%
## 558: 19.69% 61.18% 30.72% 7.57% 25.93% 52.70%
## 559: 19.69% 61.18% 30.93% 7.57% 25.78% 52.70%
## 560: 19.65% 61.18% 31.36% 7.41% 25.63% 52.70%
## 561: 19.69% 61.18% 31.14% 7.49% 25.78% 52.70%
## 562: 19.53% 61.18% 30.93% 7.41% 25.48% 52.70%
## 563: 19.57% 61.18% 31.14% 7.41% 25.48% 52.70%
## 564: 19.65% 61.18% 31.36% 7.41% 25.63% 52.70%
## 565: 19.57% 61.18% 30.72% 7.57% 25.48% 52.70%
## 566: 19.57% 61.18% 31.14% 7.49% 25.34% 52.70%
## 567: 19.53% 61.18% 31.14% 7.49% 25.34% 51.35%
## 568: 19.46% 61.18% 30.93% 7.49% 25.19% 51.35%
## 569: 19.42% 61.18% 30.51% 7.57% 25.04% 52.70%
## 570: 19.61% 61.18% 30.72% 7.57% 25.48% 54.05%
## 571: 19.61% 60.00% 30.93% 7.57% 25.48% 54.05%
## 572: 19.38% 60.00% 30.51% 7.41% 25.34% 52.70%
## 573: 19.61% 60.00% 30.93% 7.49% 25.78% 52.70%
## 574: 19.46% 60.00% 30.72% 7.33% 25.63% 52.70%
## 575: 19.49% 60.00% 30.93% 7.41% 25.48% 52.70%
## 576: 19.46% 60.00% 30.72% 7.41% 25.34% 54.05%
## 577: 19.46% 60.00% 30.72% 7.41% 25.48% 52.70%
## 578: 19.46% 60.00% 30.72% 7.41% 25.34% 54.05%
## 579: 19.46% 60.00% 30.51% 7.49% 25.34% 54.05%
## 580: 19.26% 60.00% 30.51% 7.26% 25.04% 54.05%
## 581: 19.26% 60.00% 30.51% 7.26% 25.04% 54.05%
## 582: 19.34% 60.00% 30.51% 7.02% 25.78% 54.05%
## 583: 19.53% 60.00% 30.72% 7.33% 25.78% 54.05%
## 584: 19.38% 60.00% 30.51% 7.10% 25.78% 54.05%
## 585: 19.34% 60.00% 30.30% 7.26% 25.48% 54.05%
## 586: 19.38% 60.00% 30.51% 7.18% 25.78% 52.70%
## 587: 19.22% 60.00% 30.08% 7.18% 25.48% 52.70%
## 588: 19.34% 60.00% 30.30% 7.26% 25.63% 52.70%
## 589: 19.30% 61.18% 30.30% 7.18% 25.48% 52.70%
## 590: 19.42% 60.00% 30.51% 7.26% 25.78% 52.70%
## 591: 19.42% 60.00% 30.30% 7.33% 25.78% 52.70%
## 592: 19.30% 60.00% 30.30% 7.18% 25.63% 52.70%
## 593: 19.38% 60.00% 30.30% 7.26% 25.78% 52.70%
## 594: 19.38% 60.00% 30.51% 7.26% 25.63% 52.70%
## 595: 19.46% 60.00% 30.51% 7.33% 25.78% 52.70%
## 596: 19.38% 60.00% 30.51% 7.26% 25.63% 52.70%
## 597: 19.30% 60.00% 30.51% 7.18% 25.48% 52.70%
## 598: 19.38% 60.00% 30.30% 7.26% 25.78% 52.70%
## 599: 19.30% 60.00% 30.51% 7.18% 25.48% 52.70%
## 600: 19.26% 60.00% 30.30% 7.18% 25.48% 52.70%
## 601: 19.30% 60.00% 30.51% 7.10% 25.63% 52.70%
## 602: 19.46% 60.00% 30.51% 7.26% 25.78% 54.05%
## 603: 19.49% 60.00% 30.93% 7.18% 25.78% 54.05%
## 604: 19.46% 60.00% 30.51% 7.18% 25.93% 54.05%
## 605: 19.42% 60.00% 30.51% 7.18% 25.78% 54.05%
## 606: 19.46% 60.00% 30.72% 7.18% 25.78% 54.05%
## 607: 19.42% 60.00% 30.51% 7.18% 25.78% 54.05%
## 608: 19.42% 60.00% 30.72% 7.26% 25.48% 54.05%
## 609: 19.46% 60.00% 30.93% 7.10% 25.78% 54.05%
## 610: 19.53% 60.00% 30.72% 7.18% 25.93% 55.41%
## 611: 19.49% 60.00% 30.72% 7.18% 25.78% 55.41%
## 612: 19.46% 60.00% 30.51% 7.18% 25.78% 55.41%
## 613: 19.61% 61.18% 30.72% 7.18% 26.08% 55.41%
## 614: 19.38% 60.00% 30.51% 7.18% 25.63% 54.05%
## 615: 19.42% 60.00% 30.72% 7.10% 25.78% 54.05%
## 616: 19.46% 60.00% 30.72% 7.18% 25.78% 54.05%
## 617: 19.42% 60.00% 30.51% 7.26% 25.63% 54.05%
## 618: 19.53% 60.00% 30.72% 7.26% 25.78% 55.41%
## 619: 19.42% 60.00% 30.51% 7.18% 25.78% 54.05%
## 620: 19.49% 60.00% 30.93% 7.18% 25.63% 55.41%
## 621: 19.57% 60.00% 30.93% 7.10% 26.08% 55.41%
## 622: 19.53% 60.00% 30.93% 7.18% 25.78% 55.41%
## 623: 19.42% 60.00% 30.72% 7.02% 25.78% 55.41%
## 624: 19.53% 60.00% 30.93% 7.18% 25.78% 55.41%
## 625: 19.61% 60.00% 30.93% 7.33% 25.78% 55.41%
## 626: 19.57% 60.00% 30.51% 7.26% 26.08% 55.41%
## 627: 19.57% 60.00% 30.51% 7.26% 26.08% 55.41%
## 628: 19.65% 60.00% 30.72% 7.18% 26.38% 55.41%
## 629: 19.73% 60.00% 30.93% 7.26% 26.38% 55.41%
## 630: 19.81% 60.00% 30.93% 7.33% 26.53% 55.41%
## 631: 19.65% 60.00% 30.72% 7.33% 26.08% 55.41%
## 632: 19.65% 60.00% 30.93% 7.18% 26.23% 55.41%
## 633: 19.61% 60.00% 30.93% 7.18% 26.08% 55.41%
## 634: 19.53% 60.00% 30.72% 7.18% 25.93% 55.41%
## 635: 19.65% 60.00% 30.93% 7.18% 26.23% 55.41%
## 636: 19.65% 60.00% 31.14% 7.18% 26.08% 55.41%
## 637: 19.69% 61.18% 31.36% 7.18% 25.93% 55.41%
## 638: 19.77% 61.18% 31.57% 7.18% 26.08% 55.41%
## 639: 19.77% 61.18% 31.36% 7.26% 26.08% 55.41%
## 640: 19.77% 61.18% 31.14% 7.33% 26.08% 55.41%
## 641: 19.77% 61.18% 31.36% 7.26% 26.08% 55.41%
## 642: 19.81% 61.18% 31.14% 7.26% 26.38% 55.41%
## 643: 19.81% 61.18% 30.93% 7.26% 26.53% 55.41%
## 644: 19.84% 61.18% 31.14% 7.26% 26.53% 55.41%
## 645: 19.77% 61.18% 30.93% 7.18% 26.53% 55.41%
## 646: 19.81% 61.18% 31.14% 7.18% 26.53% 55.41%
## 647: 19.81% 61.18% 31.36% 7.10% 26.53% 55.41%
## 648: 19.92% 62.35% 31.36% 7.18% 26.68% 55.41%
## 649: 19.88% 62.35% 31.36% 7.10% 26.68% 55.41%
## 650: 19.92% 61.18% 31.57% 7.18% 26.68% 55.41%
## 651: 19.88% 61.18% 31.78% 7.10% 26.53% 55.41%
## 652: 19.77% 61.18% 31.14% 7.02% 26.68% 55.41%
## 653: 19.81% 61.18% 31.36% 7.02% 26.68% 55.41%
## 654: 19.65% 61.18% 30.93% 7.02% 26.53% 54.05%
## 655: 19.61% 60.00% 30.93% 7.02% 26.53% 54.05%
## 656: 19.81% 61.18% 31.57% 7.02% 26.53% 55.41%
## 657: 19.77% 61.18% 31.14% 7.02% 26.68% 55.41%
## 658: 19.88% 61.18% 31.57% 7.10% 26.68% 55.41%
## 659: 19.73% 60.00% 31.14% 7.10% 26.53% 55.41%
## 660: 19.81% 61.18% 31.36% 7.10% 26.68% 54.05%
## 661: 19.73% 61.18% 31.36% 7.02% 26.53% 54.05%
## 662: 19.65% 61.18% 31.14% 7.02% 26.53% 52.70%
## 663: 19.61% 61.18% 31.14% 7.10% 26.08% 54.05%
## 664: 19.53% 61.18% 31.14% 6.94% 26.23% 52.70%
## 665: 19.65% 61.18% 31.36% 7.02% 26.38% 52.70%
## 666: 19.73% 61.18% 31.57% 7.10% 26.23% 54.05%
## 667: 19.69% 61.18% 31.57% 6.94% 26.38% 54.05%
## 668: 19.53% 61.18% 31.14% 6.94% 26.08% 54.05%
## 669: 19.69% 61.18% 31.57% 6.94% 26.38% 54.05%
## 670: 19.53% 61.18% 31.14% 6.94% 26.08% 54.05%
## 671: 19.65% 61.18% 31.57% 6.86% 26.38% 54.05%
## 672: 19.73% 61.18% 31.78% 6.94% 26.38% 54.05%
## 673: 19.69% 61.18% 31.57% 6.86% 26.53% 54.05%
## 674: 19.69% 62.35% 31.57% 6.86% 26.38% 54.05%
## 675: 19.69% 62.35% 31.57% 6.86% 26.38% 54.05%
## 676: 19.65% 62.35% 31.14% 6.86% 26.53% 54.05%
## 677: 19.57% 62.35% 31.14% 6.78% 26.38% 54.05%
## 678: 19.61% 61.18% 30.93% 6.94% 26.53% 54.05%
## 679: 19.73% 62.35% 31.36% 6.94% 26.53% 54.05%
## 680: 19.69% 61.18% 31.14% 7.02% 26.53% 54.05%
## 681: 19.57% 61.18% 30.93% 7.02% 26.38% 52.70%
## 682: 19.77% 61.18% 31.36% 7.02% 26.68% 54.05%
## 683: 19.65% 61.18% 31.36% 6.86% 26.53% 54.05%
## 684: 19.65% 61.18% 31.14% 7.02% 26.38% 54.05%
## 685: 19.61% 61.18% 31.36% 6.94% 26.23% 54.05%
## 686: 19.53% 61.18% 30.93% 6.86% 26.53% 52.70%
## 687: 19.57% 61.18% 31.14% 6.86% 26.53% 52.70%
## 688: 19.57% 61.18% 30.93% 6.94% 26.53% 52.70%
## 689: 19.57% 61.18% 31.14% 6.86% 26.53% 52.70%
## 690: 19.61% 61.18% 31.36% 6.86% 26.53% 52.70%
## 691: 19.53% 60.00% 30.93% 6.94% 26.53% 52.70%
## 692: 19.61% 61.18% 31.36% 6.86% 26.53% 52.70%
## 693: 19.53% 60.00% 31.14% 6.86% 26.53% 52.70%
## 694: 19.53% 60.00% 31.36% 6.86% 26.38% 52.70%
## 695: 19.61% 61.18% 31.36% 6.86% 26.53% 52.70%
## 696: 19.57% 61.18% 31.36% 6.78% 26.53% 52.70%
## 697: 19.61% 61.18% 31.57% 6.86% 26.38% 52.70%
## 698: 19.61% 61.18% 31.36% 6.86% 26.53% 52.70%
## 699: 19.49% 60.00% 31.14% 6.78% 26.53% 52.70%
## 700: 19.42% 60.00% 30.93% 6.70% 26.53% 52.70%
## 701: 19.53% 60.00% 31.36% 6.78% 26.53% 52.70%
## 702: 19.42% 60.00% 30.93% 6.70% 26.53% 52.70%
## 703: 19.49% 60.00% 31.36% 6.78% 26.38% 52.70%
## 704: 19.42% 60.00% 30.93% 6.70% 26.53% 52.70%
## 705: 19.57% 60.00% 31.36% 6.86% 26.53% 52.70%
## 706: 19.57% 60.00% 30.93% 6.94% 26.68% 52.70%
## 707: 19.49% 60.00% 30.93% 6.86% 26.53% 52.70%
## 708: 19.38% 60.00% 30.93% 6.86% 26.08% 52.70%
## 709: 19.57% 60.00% 31.36% 6.86% 26.53% 52.70%
## 710: 19.42% 60.00% 31.14% 6.70% 26.38% 52.70%
## 711: 19.34% 60.00% 30.93% 6.78% 26.08% 52.70%
## 712: 19.30% 61.18% 30.72% 6.70% 26.08% 52.70%
## 713: 19.42% 61.18% 31.14% 6.78% 26.08% 52.70%
## 714: 19.38% 61.18% 31.14% 6.62% 26.23% 52.70%
## 715: 19.18% 60.00% 30.72% 6.62% 25.93% 52.70%
## 716: 19.30% 61.18% 30.72% 6.78% 25.93% 52.70%
## 717: 19.34% 60.00% 31.14% 6.78% 25.93% 52.70%
## 718: 19.38% 60.00% 30.93% 7.02% 25.78% 52.70%
## 719: 19.34% 60.00% 30.93% 6.86% 25.93% 52.70%
## 720: 19.46% 60.00% 30.93% 7.02% 26.08% 52.70%
## 721: 19.46% 60.00% 31.14% 6.94% 26.08% 52.70%
## 722: 19.57% 61.18% 31.14% 7.10% 26.08% 52.70%
## 723: 19.46% 60.00% 30.72% 7.02% 26.23% 52.70%
## 724: 19.53% 60.00% 30.93% 7.10% 26.23% 52.70%
## 725: 19.53% 60.00% 30.93% 7.10% 26.23% 52.70%
## 726: 19.61% 60.00% 31.14% 7.10% 26.38% 52.70%
## 727: 19.53% 60.00% 30.93% 7.02% 26.38% 52.70%
## 728: 19.49% 60.00% 30.93% 7.02% 26.23% 52.70%
## 729: 19.53% 60.00% 30.93% 7.10% 26.23% 52.70%
## 730: 19.49% 60.00% 30.93% 7.02% 26.23% 52.70%
## 731: 19.49% 60.00% 30.93% 7.10% 26.08% 52.70%
## 732: 19.42% 60.00% 30.72% 7.02% 26.08% 52.70%
## 733: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 734: 19.49% 60.00% 30.93% 7.10% 26.08% 52.70%
## 735: 19.46% 60.00% 30.93% 6.94% 26.23% 52.70%
## 736: 19.42% 60.00% 30.93% 6.86% 26.23% 52.70%
## 737: 19.34% 60.00% 30.72% 6.86% 26.08% 52.70%
## 738: 19.53% 60.00% 31.36% 7.02% 26.08% 52.70%
## 739: 19.42% 60.00% 30.93% 6.94% 26.08% 52.70%
## 740: 19.46% 60.00% 31.36% 6.94% 25.93% 52.70%
## 741: 19.38% 60.00% 30.93% 6.86% 26.08% 52.70%
## 742: 19.42% 60.00% 30.93% 6.94% 26.08% 52.70%
## 743: 19.46% 60.00% 31.36% 6.78% 26.23% 52.70%
## 744: 19.53% 60.00% 31.36% 6.86% 26.38% 52.70%
## 745: 19.38% 60.00% 30.93% 6.86% 26.08% 52.70%
## 746: 19.38% 60.00% 31.14% 6.86% 25.93% 52.70%
## 747: 19.46% 60.00% 31.14% 6.86% 26.23% 52.70%
## 748: 19.42% 60.00% 31.14% 6.78% 26.23% 52.70%
## 749: 19.42% 60.00% 30.93% 6.86% 26.23% 52.70%
## 750: 19.46% 60.00% 31.14% 6.86% 26.23% 52.70%
## 751: 19.38% 60.00% 31.14% 6.78% 26.08% 52.70%
## 752: 19.46% 60.00% 31.14% 6.86% 26.23% 52.70%
## 753: 19.49% 60.00% 31.14% 6.94% 26.23% 52.70%
## 754: 19.34% 60.00% 30.72% 6.78% 26.23% 52.70%
## 755: 19.34% 60.00% 30.72% 6.78% 26.23% 52.70%
## 756: 19.38% 60.00% 30.72% 6.78% 26.38% 52.70%
## 757: 19.30% 60.00% 30.51% 6.70% 26.38% 52.70%
## 758: 19.49% 60.00% 31.14% 6.86% 26.38% 52.70%
## 759: 19.30% 60.00% 30.72% 6.78% 26.08% 52.70%
## 760: 19.34% 60.00% 30.72% 6.78% 26.23% 52.70%
## 761: 19.26% 60.00% 30.72% 6.70% 26.08% 52.70%
## 762: 19.34% 60.00% 30.72% 6.86% 26.08% 52.70%
## 763: 19.30% 60.00% 30.93% 6.70% 26.08% 52.70%
## 764: 19.26% 60.00% 30.93% 6.70% 25.93% 52.70%
## 765: 19.26% 60.00% 30.72% 6.70% 26.08% 52.70%
## 766: 19.26% 60.00% 30.72% 6.62% 26.23% 52.70%
## 767: 19.22% 60.00% 30.72% 6.62% 26.08% 52.70%
## 768: 19.34% 60.00% 30.72% 6.62% 26.53% 52.70%
## 769: 19.30% 60.00% 30.72% 6.62% 26.38% 52.70%
## 770: 19.26% 60.00% 30.30% 6.62% 26.53% 52.70%
## 771: 19.34% 60.00% 30.51% 6.70% 26.53% 52.70%
## 772: 19.30% 60.00% 30.72% 6.70% 26.23% 52.70%
## 773: 19.18% 60.00% 30.30% 6.70% 26.08% 52.70%
## 774: 19.22% 60.00% 30.30% 6.62% 26.38% 52.70%
## 775: 19.34% 60.00% 30.93% 6.70% 26.23% 52.70%
## 776: 19.18% 60.00% 30.72% 6.70% 25.78% 52.70%
## 777: 19.30% 60.00% 30.72% 6.78% 26.08% 52.70%
## 778: 19.26% 60.00% 30.51% 6.78% 26.08% 52.70%
## 779: 19.18% 60.00% 30.72% 6.78% 25.63% 52.70%
## 780: 19.18% 60.00% 30.51% 6.78% 25.78% 52.70%
## 781: 19.26% 60.00% 30.72% 6.70% 26.08% 52.70%
## 782: 19.22% 60.00% 30.51% 6.70% 26.08% 52.70%
## 783: 19.26% 60.00% 30.72% 6.70% 26.08% 52.70%
## 784: 19.30% 60.00% 30.72% 6.86% 25.93% 52.70%
## 785: 19.30% 60.00% 30.72% 6.78% 26.08% 52.70%
## 786: 19.30% 60.00% 30.93% 6.62% 26.23% 52.70%
## 787: 19.30% 60.00% 30.72% 6.70% 26.23% 52.70%
## 788: 19.11% 60.00% 30.30% 6.62% 25.93% 52.70%
## 789: 19.14% 60.00% 30.30% 6.70% 25.93% 52.70%
## 790: 19.14% 60.00% 30.30% 6.70% 25.93% 52.70%
## 791: 19.18% 60.00% 30.51% 6.70% 25.93% 52.70%
## 792: 19.14% 60.00% 30.30% 6.70% 25.93% 52.70%
## 793: 19.14% 60.00% 30.51% 6.70% 25.78% 52.70%
## 794: 19.14% 60.00% 30.51% 6.70% 25.78% 52.70%
## 795: 19.14% 60.00% 30.51% 6.62% 25.93% 52.70%
## 796: 19.14% 60.00% 30.30% 6.62% 26.08% 52.70%
## 797: 19.22% 60.00% 30.08% 6.94% 25.93% 52.70%
## 798: 19.11% 60.00% 30.08% 6.70% 25.93% 52.70%
## 799: 19.18% 60.00% 30.08% 6.86% 25.93% 52.70%
## 800: 19.42% 60.00% 30.30% 7.10% 26.23% 52.70%
## 801: 19.34% 60.00% 30.30% 6.94% 26.23% 52.70%
## 802: 19.26% 60.00% 30.30% 6.86% 26.08% 52.70%
## 803: 19.34% 60.00% 30.30% 6.86% 26.38% 52.70%
## 804: 19.22% 60.00% 30.08% 6.78% 26.23% 52.70%
## 805: 19.26% 60.00% 30.08% 6.78% 26.38% 52.70%
## 806: 19.30% 60.00% 30.30% 6.86% 26.23% 52.70%
## 807: 19.34% 60.00% 30.51% 6.86% 26.23% 52.70%
## 808: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 809: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 810: 19.30% 60.00% 30.51% 6.86% 26.08% 52.70%
## 811: 19.26% 60.00% 30.30% 6.94% 25.93% 52.70%
## 812: 19.26% 60.00% 30.30% 6.86% 26.08% 52.70%
## 813: 19.26% 60.00% 30.30% 6.78% 26.23% 52.70%
## 814: 19.22% 60.00% 30.30% 6.78% 26.08% 52.70%
## 815: 19.22% 60.00% 30.30% 6.78% 26.08% 52.70%
## 816: 19.22% 60.00% 30.30% 6.78% 26.08% 52.70%
## 817: 19.30% 60.00% 30.30% 7.02% 25.93% 52.70%
## 818: 19.38% 61.18% 30.51% 7.02% 25.93% 52.70%
## 819: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 820: 19.38% 60.00% 30.51% 7.02% 26.08% 52.70%
## 821: 19.30% 60.00% 30.51% 6.94% 25.93% 52.70%
## 822: 19.38% 60.00% 30.51% 6.94% 26.23% 52.70%
## 823: 19.22% 60.00% 30.30% 6.70% 26.23% 52.70%
## 824: 19.30% 60.00% 30.30% 6.78% 26.38% 52.70%
## 825: 19.26% 60.00% 30.51% 6.70% 26.23% 52.70%
## 826: 19.34% 60.00% 30.30% 6.94% 26.23% 52.70%
## 827: 19.34% 60.00% 30.30% 6.94% 26.23% 52.70%
## 828: 19.34% 60.00% 30.08% 6.94% 26.38% 52.70%
## 829: 19.38% 60.00% 30.51% 6.94% 26.23% 52.70%
## 830: 19.26% 60.00% 30.08% 6.94% 26.08% 52.70%
## 831: 19.22% 60.00% 30.08% 6.94% 25.93% 52.70%
## 832: 19.26% 60.00% 30.30% 6.94% 25.93% 52.70%
## 833: 19.26% 60.00% 30.30% 6.94% 25.93% 52.70%
## 834: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 835: 19.30% 60.00% 30.51% 6.78% 26.23% 52.70%
## 836: 19.22% 60.00% 30.30% 6.70% 26.23% 52.70%
## 837: 19.22% 60.00% 30.30% 6.86% 25.93% 52.70%
## 838: 19.22% 60.00% 30.30% 6.86% 25.93% 52.70%
## 839: 19.26% 60.00% 30.30% 6.78% 26.23% 52.70%
## 840: 19.18% 60.00% 30.30% 6.78% 25.93% 52.70%
## 841: 19.18% 60.00% 30.30% 6.70% 26.08% 52.70%
## 842: 19.14% 60.00% 30.08% 6.78% 25.93% 52.70%
## 843: 19.14% 60.00% 30.08% 6.86% 25.78% 52.70%
## 844: 19.18% 60.00% 29.87% 6.94% 25.93% 52.70%
## 845: 19.30% 60.00% 30.51% 6.86% 26.08% 52.70%
## 846: 19.26% 60.00% 30.30% 6.94% 25.93% 52.70%
## 847: 19.30% 60.00% 30.30% 6.94% 26.08% 52.70%
## 848: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 849: 19.26% 60.00% 30.30% 6.86% 26.08% 52.70%
## 850: 19.30% 60.00% 30.51% 6.94% 25.93% 52.70%
## 851: 19.30% 60.00% 30.30% 6.94% 26.08% 52.70%
## 852: 19.22% 60.00% 30.08% 6.94% 25.93% 52.70%
## 853: 19.26% 60.00% 30.30% 6.94% 25.93% 52.70%
## 854: 19.26% 60.00% 30.08% 6.94% 26.08% 52.70%
## 855: 19.26% 60.00% 30.08% 6.94% 26.08% 52.70%
## 856: 19.26% 60.00% 30.08% 6.94% 26.08% 52.70%
## 857: 19.26% 60.00% 29.87% 6.94% 26.23% 52.70%
## 858: 19.22% 60.00% 29.87% 6.86% 26.23% 52.70%
## 859: 19.18% 60.00% 29.87% 6.86% 26.08% 52.70%
## 860: 19.18% 60.00% 29.87% 6.86% 26.08% 52.70%
## 861: 19.22% 60.00% 30.30% 6.86% 25.93% 52.70%
## 862: 19.11% 60.00% 29.45% 6.86% 26.08% 52.70%
## 863: 19.18% 60.00% 29.66% 6.86% 26.23% 52.70%
## 864: 19.14% 60.00% 29.66% 6.86% 26.08% 52.70%
## 865: 19.18% 60.00% 30.08% 6.94% 25.78% 52.70%
## 866: 19.26% 60.00% 30.08% 6.78% 26.38% 52.70%
## 867: 19.11% 61.18% 29.87% 6.70% 25.93% 52.70%
## 868: 19.18% 60.00% 30.08% 6.70% 26.23% 52.70%
## 869: 19.18% 60.00% 30.08% 6.70% 26.23% 52.70%
## 870: 19.11% 60.00% 29.87% 6.70% 26.08% 52.70%
## 871: 19.03% 60.00% 29.66% 6.70% 25.93% 52.70%
## 872: 19.14% 60.00% 29.87% 6.78% 26.08% 52.70%
## 873: 19.11% 60.00% 29.87% 6.78% 25.93% 52.70%
## 874: 19.22% 61.18% 29.87% 6.78% 26.23% 52.70%
## 875: 19.07% 61.18% 29.66% 6.70% 25.93% 52.70%
## 876: 19.18% 61.18% 29.87% 6.78% 25.93% 54.05%
## 877: 19.26% 61.18% 29.87% 6.78% 26.23% 54.05%
## 878: 19.26% 61.18% 30.30% 6.78% 25.93% 54.05%
## 879: 19.22% 61.18% 30.08% 6.78% 25.93% 54.05%
## 880: 19.22% 61.18% 29.87% 6.70% 26.23% 54.05%
## 881: 19.30% 61.18% 30.30% 6.70% 26.23% 54.05%
## 882: 19.18% 61.18% 29.87% 6.78% 25.93% 54.05%
## 883: 19.26% 61.18% 30.08% 6.70% 26.23% 54.05%
## 884: 19.18% 61.18% 30.51% 6.70% 25.78% 52.70%
## 885: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 886: 19.11% 61.18% 30.08% 6.70% 25.78% 52.70%
## 887: 19.22% 61.18% 30.51% 6.70% 25.93% 52.70%
## 888: 19.22% 61.18% 30.30% 6.70% 25.93% 54.05%
## 889: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 890: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 891: 19.18% 61.18% 30.30% 6.62% 26.08% 52.70%
## 892: 19.14% 61.18% 30.08% 6.70% 25.93% 52.70%
## 893: 19.18% 61.18% 30.08% 6.78% 25.93% 52.70%
## 894: 19.11% 61.18% 30.30% 6.62% 25.78% 52.70%
## 895: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 896: 19.18% 61.18% 30.30% 6.78% 25.78% 52.70%
## 897: 19.18% 61.18% 30.30% 6.78% 25.78% 52.70%
## 898: 19.18% 61.18% 30.30% 6.78% 25.78% 52.70%
## 899: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 900: 19.14% 61.18% 30.30% 6.78% 25.63% 52.70%
## 901: 19.11% 61.18% 30.08% 6.78% 25.63% 52.70%
## 902: 19.14% 61.18% 30.08% 6.70% 25.93% 52.70%
## 903: 19.18% 61.18% 30.08% 6.78% 25.93% 52.70%
## 904: 19.07% 61.18% 30.08% 6.70% 25.63% 52.70%
## 905: 19.18% 61.18% 30.30% 6.78% 25.78% 52.70%
## 906: 19.22% 61.18% 30.30% 6.78% 25.93% 52.70%
## 907: 19.18% 61.18% 30.08% 6.78% 25.93% 52.70%
## 908: 19.11% 61.18% 30.30% 6.70% 25.63% 52.70%
## 909: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 910: 19.18% 61.18% 30.30% 6.78% 25.78% 52.70%
## 911: 19.11% 61.18% 30.30% 6.62% 25.78% 52.70%
## 912: 19.07% 61.18% 30.08% 6.62% 25.78% 52.70%
## 913: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 914: 19.07% 61.18% 30.08% 6.70% 25.63% 52.70%
## 915: 19.11% 61.18% 30.30% 6.62% 25.78% 52.70%
## 916: 19.07% 61.18% 30.08% 6.70% 25.63% 52.70%
## 917: 18.99% 61.18% 30.08% 6.55% 25.63% 52.70%
## 918: 19.07% 61.18% 30.30% 6.55% 25.78% 52.70%
## 919: 19.11% 62.35% 30.30% 6.55% 25.78% 52.70%
## 920: 19.11% 61.18% 30.30% 6.55% 25.93% 52.70%
## 921: 19.07% 61.18% 30.30% 6.55% 25.78% 52.70%
## 922: 19.03% 61.18% 30.30% 6.47% 25.78% 52.70%
## 923: 19.03% 61.18% 30.30% 6.47% 25.78% 52.70%
## 924: 18.99% 61.18% 30.30% 6.55% 25.48% 52.70%
## 925: 18.95% 61.18% 30.30% 6.55% 25.34% 52.70%
## 926: 18.99% 61.18% 30.30% 6.55% 25.48% 52.70%
## 927: 19.03% 61.18% 30.51% 6.55% 25.48% 52.70%
## 928: 19.03% 61.18% 30.51% 6.55% 25.48% 52.70%
## 929: 18.99% 61.18% 30.51% 6.55% 25.34% 52.70%
## 930: 18.91% 61.18% 30.30% 6.55% 25.19% 52.70%
## 931: 18.91% 61.18% 30.08% 6.62% 25.19% 52.70%
## 932: 18.91% 61.18% 30.30% 6.62% 25.04% 52.70%
## 933: 18.95% 61.18% 30.08% 6.62% 25.34% 52.70%
## 934: 19.03% 61.18% 30.30% 6.70% 25.34% 52.70%
## 935: 18.99% 61.18% 30.30% 6.62% 25.34% 52.70%
## 936: 18.99% 61.18% 30.51% 6.62% 25.19% 52.70%
## 937: 18.95% 61.18% 30.51% 6.62% 25.04% 52.70%
## 938: 19.03% 61.18% 30.51% 6.78% 25.04% 52.70%
## 939: 19.03% 61.18% 30.51% 6.70% 25.19% 52.70%
## 940: 19.18% 62.35% 30.72% 6.78% 25.34% 52.70%
## 941: 19.07% 62.35% 30.51% 6.70% 25.19% 52.70%
## 942: 19.14% 62.35% 30.51% 6.86% 25.19% 52.70%
## 943: 19.14% 62.35% 30.51% 6.70% 25.48% 52.70%
## 944: 19.18% 62.35% 30.72% 6.70% 25.48% 52.70%
## 945: 19.11% 62.35% 30.30% 6.78% 25.34% 52.70%
## 946: 19.07% 62.35% 30.51% 6.78% 25.04% 52.70%
## 947: 19.11% 62.35% 30.30% 6.86% 25.19% 52.70%
## 948: 19.22% 62.35% 30.51% 6.94% 25.34% 52.70%
## 949: 19.14% 62.35% 30.30% 6.86% 25.34% 52.70%
## 950: 19.26% 62.35% 30.30% 7.02% 25.48% 52.70%
## 951: 19.22% 62.35% 30.51% 6.94% 25.34% 52.70%
## 952: 19.14% 62.35% 30.51% 6.86% 25.19% 52.70%
## 953: 19.26% 62.35% 30.72% 6.94% 25.34% 52.70%
## 954: 19.18% 62.35% 30.51% 6.86% 25.34% 52.70%
## 955: 19.14% 62.35% 30.51% 6.86% 25.19% 52.70%
## 956: 19.18% 62.35% 30.72% 6.86% 25.19% 52.70%
## 957: 19.14% 62.35% 30.72% 6.86% 25.04% 52.70%
## 958: 19.22% 62.35% 30.93% 6.86% 25.19% 52.70%
## 959: 19.22% 62.35% 30.72% 6.94% 25.19% 52.70%
## 960: 19.26% 62.35% 30.93% 6.94% 25.19% 52.70%
## 961: 19.22% 62.35% 30.72% 6.94% 25.19% 52.70%
## 962: 19.26% 62.35% 30.72% 7.02% 25.19% 52.70%
## 963: 19.18% 62.35% 30.51% 7.02% 25.04% 52.70%
## 964: 19.14% 62.35% 30.51% 6.94% 25.04% 52.70%
## 965: 19.22% 62.35% 30.93% 6.86% 25.19% 52.70%
## 966: 19.22% 62.35% 30.72% 6.94% 25.19% 52.70%
## 967: 19.22% 62.35% 30.72% 6.94% 25.19% 52.70%
## 968: 19.14% 62.35% 30.72% 6.86% 25.04% 52.70%
## 969: 19.26% 62.35% 30.93% 6.86% 25.34% 52.70%
## 970: 19.26% 62.35% 30.51% 6.94% 25.48% 52.70%
## 971: 19.34% 62.35% 30.72% 7.02% 25.48% 52.70%
## 972: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 973: 19.34% 62.35% 30.51% 7.02% 25.63% 52.70%
## 974: 19.34% 62.35% 30.93% 6.94% 25.48% 52.70%
## 975: 19.34% 62.35% 30.72% 6.94% 25.63% 52.70%
## 976: 19.34% 62.35% 30.93% 6.94% 25.48% 52.70%
## 977: 19.34% 62.35% 30.93% 6.94% 25.48% 52.70%
## 978: 19.38% 61.18% 30.72% 6.94% 25.93% 52.70%
## 979: 19.38% 62.35% 30.72% 6.86% 25.93% 52.70%
## 980: 19.34% 62.35% 30.72% 6.86% 25.78% 52.70%
## 981: 19.34% 61.18% 30.93% 6.86% 25.78% 52.70%
## 982: 19.34% 61.18% 30.93% 6.94% 25.63% 52.70%
## 983: 19.30% 61.18% 30.72% 6.94% 25.63% 52.70%
## 984: 19.30% 61.18% 30.72% 6.94% 25.63% 52.70%
## 985: 19.22% 61.18% 30.51% 6.86% 25.63% 52.70%
## 986: 19.22% 61.18% 30.51% 6.86% 25.63% 52.70%
## 987: 19.30% 61.18% 30.93% 6.78% 25.78% 52.70%
## 988: 19.38% 62.35% 30.72% 6.86% 25.93% 52.70%
## 989: 19.46% 62.35% 30.72% 6.94% 26.08% 52.70%
## 990: 19.26% 61.18% 30.72% 6.94% 25.48% 52.70%
## 991: 19.34% 61.18% 30.72% 6.94% 25.78% 52.70%
## 992: 19.26% 61.18% 30.93% 6.86% 25.48% 52.70%
## 993: 19.34% 62.35% 30.93% 6.86% 25.63% 52.70%
## 994: 19.30% 61.18% 31.14% 6.86% 25.48% 52.70%
## 995: 19.26% 61.18% 30.93% 6.86% 25.48% 52.70%
## 996: 19.38% 62.35% 30.93% 6.94% 25.63% 52.70%
## 997: 19.38% 61.18% 30.93% 6.94% 25.78% 52.70%
## 998: 19.38% 61.18% 31.14% 6.94% 25.63% 52.70%
## 999: 19.34% 61.18% 30.93% 6.94% 25.63% 52.70%
## 1000: 19.38% 61.18% 30.93% 6.94% 25.78% 52.70%
## 1001: 19.42% 62.35% 30.93% 6.94% 25.78% 52.70%
## 1002: 19.42% 62.35% 30.93% 6.94% 25.78% 52.70%
## 1003: 19.38% 62.35% 30.93% 6.86% 25.78% 52.70%
## 1004: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 1005: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 1006: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 1007: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 1008: 19.38% 62.35% 30.51% 7.02% 25.78% 52.70%
## 1009: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1010: 19.30% 62.35% 30.51% 6.94% 25.63% 52.70%
## 1011: 19.34% 62.35% 30.51% 6.94% 25.78% 52.70%
## 1012: 19.30% 62.35% 30.72% 6.78% 25.78% 52.70%
## 1013: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1014: 19.34% 62.35% 30.72% 6.86% 25.78% 52.70%
## 1015: 19.30% 62.35% 30.51% 6.94% 25.63% 52.70%
## 1016: 19.30% 62.35% 30.72% 6.94% 25.48% 52.70%
## 1017: 19.26% 62.35% 30.72% 6.86% 25.48% 52.70%
## 1018: 19.26% 62.35% 30.72% 6.94% 25.34% 52.70%
## 1019: 19.30% 62.35% 30.93% 6.78% 25.63% 52.70%
## 1020: 19.30% 62.35% 30.72% 6.86% 25.63% 52.70%
## 1021: 19.38% 62.35% 30.72% 7.02% 25.63% 52.70%
## 1022: 19.34% 62.35% 30.72% 6.94% 25.63% 52.70%
## 1023: 19.22% 62.35% 30.51% 6.86% 25.48% 52.70%
## 1024: 19.14% 62.35% 30.30% 6.86% 25.34% 52.70%
## 1025: 19.22% 62.35% 30.30% 6.94% 25.48% 52.70%
## 1026: 19.30% 62.35% 30.72% 6.86% 25.63% 52.70%
## 1027: 19.30% 62.35% 30.51% 6.94% 25.63% 52.70%
## 1028: 19.26% 62.35% 30.30% 7.02% 25.48% 52.70%
## 1029: 19.34% 62.35% 30.72% 7.02% 25.48% 52.70%
## 1030: 19.22% 62.35% 30.72% 6.86% 25.34% 52.70%
## 1031: 19.38% 62.35% 30.93% 7.10% 25.34% 52.70%
## 1032: 19.46% 62.35% 31.14% 7.02% 25.63% 52.70%
## 1033: 19.30% 62.35% 30.93% 6.94% 25.34% 52.70%
## 1034: 19.26% 62.35% 30.72% 6.94% 25.34% 52.70%
## 1035: 19.30% 62.35% 30.72% 6.94% 25.48% 52.70%
## 1036: 19.26% 62.35% 30.72% 6.86% 25.48% 52.70%
## 1037: 19.30% 62.35% 30.72% 6.94% 25.48% 52.70%
## 1038: 19.30% 62.35% 30.72% 6.94% 25.48% 52.70%
## 1039: 19.30% 62.35% 30.72% 6.86% 25.63% 52.70%
## 1040: 19.34% 62.35% 30.93% 6.94% 25.48% 52.70%
## 1041: 19.30% 62.35% 30.93% 6.86% 25.48% 52.70%
## 1042: 19.42% 62.35% 31.14% 7.02% 25.48% 52.70%
## 1043: 19.38% 62.35% 30.72% 7.02% 25.63% 52.70%
## 1044: 19.46% 62.35% 31.14% 7.10% 25.48% 52.70%
## 1045: 19.34% 62.35% 30.93% 7.02% 25.34% 52.70%
## 1046: 19.46% 62.35% 30.93% 7.10% 25.63% 52.70%
## 1047: 19.42% 62.35% 30.72% 7.10% 25.63% 52.70%
## 1048: 19.46% 62.35% 30.72% 7.10% 25.78% 52.70%
## 1049: 19.42% 62.35% 30.72% 7.10% 25.63% 52.70%
## 1050: 19.38% 62.35% 30.51% 7.02% 25.78% 52.70%
## 1051: 19.34% 62.35% 30.51% 7.02% 25.63% 52.70%
## 1052: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 1053: 19.38% 62.35% 30.51% 7.10% 25.63% 52.70%
## 1054: 19.34% 62.35% 30.72% 7.02% 25.48% 52.70%
## 1055: 19.46% 62.35% 30.72% 7.10% 25.78% 52.70%
## 1056: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1057: 19.30% 62.35% 30.72% 6.94% 25.48% 52.70%
## 1058: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1059: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1060: 19.46% 62.35% 30.93% 6.94% 25.93% 52.70%
## 1061: 19.46% 62.35% 30.93% 6.94% 25.93% 52.70%
## 1062: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1063: 19.38% 62.35% 30.51% 6.94% 25.93% 52.70%
## 1064: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1065: 19.42% 62.35% 30.72% 6.94% 25.93% 52.70%
## 1066: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1067: 19.30% 62.35% 30.30% 6.86% 25.93% 52.70%
## 1068: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1069: 19.30% 62.35% 30.30% 6.86% 25.93% 52.70%
## 1070: 19.46% 62.35% 30.93% 6.94% 25.93% 52.70%
## 1071: 19.38% 62.35% 30.72% 6.86% 25.93% 52.70%
## 1072: 19.49% 62.35% 30.93% 6.94% 26.08% 52.70%
## 1073: 19.46% 62.35% 30.72% 6.94% 26.08% 52.70%
## 1074: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1075: 19.30% 62.35% 30.30% 6.86% 25.93% 52.70%
## 1076: 19.38% 63.53% 30.51% 6.86% 25.93% 52.70%
## 1077: 19.34% 63.53% 30.30% 6.86% 25.93% 52.70%
## 1078: 19.38% 63.53% 30.30% 6.86% 26.08% 52.70%
## 1079: 19.38% 62.35% 30.30% 6.94% 26.08% 52.70%
## 1080: 19.26% 62.35% 30.08% 6.86% 25.93% 52.70%
## 1081: 19.30% 62.35% 30.08% 6.94% 25.93% 52.70%
## 1082: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1083: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1084: 19.30% 62.35% 30.08% 6.94% 25.93% 52.70%
## 1085: 19.42% 62.35% 30.51% 6.94% 26.08% 52.70%
## 1086: 19.22% 61.18% 30.08% 6.94% 25.78% 52.70%
## 1087: 19.22% 61.18% 30.30% 6.86% 25.78% 52.70%
## 1088: 19.18% 61.18% 30.08% 6.86% 25.78% 52.70%
## 1089: 19.14% 61.18% 30.08% 6.86% 25.63% 52.70%
## 1090: 19.18% 61.18% 30.08% 6.78% 25.93% 52.70%
## 1091: 19.34% 61.18% 30.08% 7.02% 26.08% 52.70%
## 1092: 19.18% 61.18% 29.87% 6.94% 25.78% 52.70%
## 1093: 19.22% 61.18% 30.30% 6.78% 25.93% 52.70%
## 1094: 19.22% 61.18% 30.08% 6.86% 25.93% 52.70%
## 1095: 19.22% 61.18% 30.08% 6.86% 25.93% 52.70%
## 1096: 19.38% 61.18% 30.72% 6.86% 26.08% 52.70%
## 1097: 19.26% 61.18% 30.30% 6.86% 25.93% 52.70%
## 1098: 19.30% 62.35% 29.87% 6.94% 26.08% 52.70%
## 1099: 19.38% 61.18% 30.72% 6.94% 25.93% 52.70%
## 1100: 19.34% 62.35% 30.30% 6.86% 26.08% 52.70%
## 1101: 19.26% 61.18% 30.30% 6.86% 25.93% 52.70%
## 1102: 19.30% 61.18% 30.30% 6.94% 25.93% 52.70%
## 1103: 19.26% 61.18% 30.30% 6.86% 25.93% 52.70%
## 1104: 19.30% 61.18% 30.30% 6.94% 25.93% 52.70%
## 1105: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1106: 19.49% 62.35% 30.93% 6.94% 26.08% 52.70%
## 1107: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1108: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1109: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1110: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1111: 19.38% 62.35% 30.51% 6.94% 25.93% 52.70%
## 1112: 19.30% 62.35% 30.30% 6.94% 25.78% 52.70%
## 1113: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1114: 19.34% 62.35% 30.51% 6.94% 25.78% 52.70%
## 1115: 19.38% 62.35% 30.51% 7.02% 25.78% 52.70%
## 1116: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1117: 19.46% 64.71% 30.51% 7.02% 25.78% 52.70%
## 1118: 19.38% 62.35% 30.51% 7.02% 25.78% 52.70%
## 1119: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1120: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1121: 19.46% 64.71% 30.30% 7.10% 25.78% 52.70%
## 1122: 19.42% 64.71% 30.30% 7.02% 25.78% 52.70%
## 1123: 19.42% 64.71% 30.30% 7.02% 25.78% 52.70%
## 1124: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1125: 19.46% 64.71% 30.30% 7.10% 25.78% 52.70%
## 1126: 19.38% 64.71% 30.08% 7.02% 25.78% 52.70%
## 1127: 19.42% 64.71% 30.30% 7.02% 25.78% 52.70%
## 1128: 19.34% 64.71% 30.08% 7.02% 25.63% 52.70%
## 1129: 19.38% 64.71% 30.30% 7.02% 25.63% 52.70%
## 1130: 19.34% 64.71% 30.30% 7.02% 25.48% 52.70%
## 1131: 19.34% 64.71% 30.30% 7.02% 25.48% 52.70%
## 1132: 19.30% 64.71% 30.08% 7.02% 25.48% 52.70%
## 1133: 19.26% 64.71% 30.30% 6.86% 25.48% 52.70%
## 1134: 19.34% 64.71% 30.51% 6.86% 25.63% 52.70%
## 1135: 19.30% 63.53% 30.30% 6.94% 25.63% 52.70%
## 1136: 19.26% 63.53% 30.30% 6.86% 25.63% 52.70%
## 1137: 19.18% 63.53% 30.30% 6.86% 25.34% 52.70%
## 1138: 19.22% 63.53% 30.08% 6.94% 25.48% 52.70%
## 1139: 19.30% 64.71% 30.30% 6.94% 25.48% 52.70%
## 1140: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1141: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1142: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1143: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1144: 19.34% 63.53% 30.72% 6.86% 25.63% 52.70%
## 1145: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1146: 19.34% 63.53% 30.51% 6.86% 25.78% 52.70%
## 1147: 19.30% 63.53% 30.51% 6.86% 25.63% 52.70%
## 1148: 19.34% 63.53% 30.51% 6.94% 25.63% 52.70%
## 1149: 19.26% 63.53% 30.30% 6.86% 25.63% 52.70%
## 1150: 19.34% 64.71% 30.30% 6.86% 25.78% 52.70%
## 1151: 19.34% 63.53% 30.51% 6.86% 25.78% 52.70%
## 1152: 19.42% 63.53% 30.93% 6.94% 25.63% 52.70%
## 1153: 19.30% 63.53% 30.72% 6.86% 25.48% 52.70%
## 1154: 19.34% 63.53% 30.72% 6.94% 25.48% 52.70%
## 1155: 19.34% 63.53% 30.72% 6.94% 25.48% 52.70%
## 1156: 19.46% 63.53% 30.93% 6.94% 25.78% 52.70%
## 1157: 19.42% 63.53% 30.93% 6.86% 25.78% 52.70%
## 1158: 19.42% 63.53% 30.93% 6.86% 25.78% 52.70%
## 1159: 19.46% 63.53% 30.93% 6.94% 25.78% 52.70%
## 1160: 19.46% 63.53% 30.93% 6.94% 25.78% 52.70%
## 1161: 19.46% 63.53% 30.93% 6.94% 25.78% 52.70%
## 1162: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1163: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1164: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1165: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1166: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1167: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1168: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1169: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1170: 19.34% 63.53% 30.51% 6.94% 25.63% 52.70%
## 1171: 19.38% 63.53% 30.51% 7.02% 25.63% 52.70%
## 1172: 19.34% 63.53% 30.30% 7.02% 25.63% 52.70%
## 1173: 19.22% 63.53% 30.08% 6.94% 25.48% 52.70%
## 1174: 19.34% 63.53% 30.30% 7.02% 25.63% 52.70%
## 1175: 19.30% 64.71% 30.08% 6.94% 25.63% 52.70%
## 1176: 19.38% 64.71% 30.30% 7.02% 25.63% 52.70%
## 1177: 19.30% 64.71% 30.08% 6.94% 25.63% 52.70%
## 1178: 19.30% 64.71% 30.08% 6.94% 25.63% 52.70%
## 1179: 19.38% 64.71% 30.51% 6.94% 25.63% 52.70%
## 1180: 19.34% 64.71% 30.51% 6.86% 25.63% 52.70%
## 1181: 19.38% 64.71% 30.51% 7.02% 25.48% 52.70%
## 1182: 19.38% 64.71% 30.51% 7.02% 25.48% 52.70%
## 1183: 19.38% 64.71% 30.51% 7.02% 25.48% 52.70%
## 1184: 19.34% 64.71% 30.51% 6.94% 25.48% 52.70%
## 1185: 19.26% 64.71% 30.51% 6.78% 25.48% 52.70%
## 1186: 19.38% 64.71% 30.30% 7.02% 25.63% 52.70%
## 1187: 19.34% 64.71% 30.51% 6.86% 25.63% 52.70%
## 1188: 19.38% 64.71% 30.51% 6.94% 25.63% 52.70%
## 1189: 19.38% 64.71% 30.72% 6.86% 25.63% 52.70%
## 1190: 19.30% 64.71% 30.30% 6.86% 25.63% 52.70%
## 1191: 19.34% 64.71% 30.51% 6.86% 25.63% 52.70%
## 1192: 19.30% 64.71% 30.30% 6.86% 25.63% 52.70%
## 1193: 19.42% 63.53% 30.93% 7.02% 25.48% 52.70%
## 1194: 19.26% 63.53% 30.08% 6.94% 25.63% 52.70%
## 1195: 19.38% 63.53% 30.51% 7.02% 25.63% 52.70%
## 1196: 19.53% 64.71% 31.14% 7.02% 25.63% 52.70%
## 1197: 19.46% 63.53% 30.93% 6.94% 25.78% 52.70%
## 1198: 19.46% 63.53% 30.72% 7.02% 25.78% 52.70%
## 1199: 19.46% 64.71% 30.72% 7.02% 25.63% 52.70%
## 1200: 19.38% 63.53% 30.72% 6.94% 25.63% 52.70%
## 1201: 19.42% 63.53% 30.93% 6.94% 25.63% 52.70%
## 1202: 19.42% 63.53% 30.72% 7.02% 25.63% 52.70%
## 1203: 19.30% 63.53% 30.72% 6.78% 25.63% 52.70%
## 1204: 19.34% 63.53% 30.72% 6.86% 25.63% 52.70%
## 1205: 19.22% 62.35% 30.51% 6.78% 25.63% 52.70%
## 1206: 19.22% 63.53% 30.08% 6.78% 25.78% 52.70%
## 1207: 19.30% 63.53% 30.51% 6.78% 25.78% 52.70%
## 1208: 19.30% 62.35% 30.51% 6.78% 25.93% 52.70%
## 1209: 19.30% 63.53% 30.51% 6.86% 25.63% 52.70%
## 1210: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1211: 19.38% 62.35% 30.51% 6.94% 25.93% 52.70%
## 1212: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1213: 19.34% 62.35% 30.72% 6.86% 25.78% 52.70%
## 1214: 19.38% 63.53% 30.51% 6.86% 25.93% 52.70%
## 1215: 19.42% 62.35% 30.72% 6.94% 25.93% 52.70%
## 1216: 19.26% 62.35% 30.08% 6.86% 25.93% 52.70%
## 1217: 19.30% 62.35% 30.08% 6.94% 25.93% 52.70%
## 1218: 19.34% 62.35% 30.08% 6.94% 26.08% 52.70%
## 1219: 19.42% 63.53% 30.08% 7.02% 26.08% 52.70%
## 1220: 19.38% 63.53% 30.08% 6.94% 26.08% 52.70%
## 1221: 19.34% 63.53% 30.08% 6.94% 25.93% 52.70%
## 1222: 19.38% 63.53% 30.08% 6.94% 26.08% 52.70%
## 1223: 19.38% 63.53% 30.08% 6.86% 26.23% 52.70%
## 1224: 19.34% 63.53% 29.87% 6.94% 26.08% 52.70%
## 1225: 19.49% 63.53% 30.72% 6.94% 26.08% 52.70%
## 1226: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1227: 19.30% 63.53% 30.51% 6.94% 25.48% 52.70%
## 1228: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1229: 19.30% 63.53% 30.51% 6.94% 25.48% 52.70%
## 1230: 19.38% 63.53% 30.51% 7.02% 25.63% 52.70%
## 1231: 19.34% 63.53% 30.51% 6.94% 25.63% 52.70%
## 1232: 19.46% 63.53% 30.51% 7.02% 25.93% 52.70%
## 1233: 19.53% 63.53% 30.72% 7.02% 26.08% 52.70%
## 1234: 19.34% 63.53% 30.30% 7.02% 25.63% 52.70%
## 1235: 19.26% 63.53% 30.08% 7.02% 25.48% 52.70%
## 1236: 19.38% 63.53% 30.08% 7.02% 25.93% 52.70%
## 1237: 19.38% 63.53% 30.08% 7.02% 25.93% 52.70%
## 1238: 19.34% 63.53% 30.08% 7.02% 25.78% 52.70%
## 1239: 19.34% 63.53% 30.08% 7.02% 25.78% 52.70%
## 1240: 19.34% 63.53% 30.08% 7.02% 25.78% 52.70%
## 1241: 19.34% 63.53% 29.87% 7.02% 25.93% 52.70%
## 1242: 19.30% 63.53% 29.87% 7.02% 25.78% 52.70%
## 1243: 19.30% 63.53% 29.87% 7.02% 25.78% 52.70%
## 1244: 19.34% 63.53% 30.08% 7.02% 25.78% 52.70%
## 1245: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1246: 19.42% 63.53% 30.30% 7.02% 25.93% 52.70%
## 1247: 19.38% 63.53% 30.08% 7.02% 25.93% 52.70%
## 1248: 19.38% 63.53% 30.30% 6.94% 25.93% 52.70%
## 1249: 19.38% 63.53% 30.30% 6.94% 25.93% 52.70%
## 1250: 19.38% 63.53% 30.30% 6.94% 25.93% 52.70%
## 1251: 19.38% 63.53% 30.30% 6.94% 25.93% 52.70%
## 1252: 19.46% 63.53% 30.51% 7.02% 25.93% 52.70%
## 1253: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1254: 19.38% 63.53% 30.30% 6.94% 25.93% 52.70%
## 1255: 19.42% 63.53% 30.30% 7.02% 25.93% 52.70%
## 1256: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1257: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1258: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1259: 19.34% 63.53% 30.51% 6.86% 25.78% 52.70%
## 1260: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1261: 19.34% 63.53% 30.51% 6.86% 25.78% 52.70%
## 1262: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1263: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1264: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1265: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1266: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1267: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1268: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1269: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1270: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1271: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1272: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1273: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1274: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1275: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1276: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1277: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1278: 19.26% 63.53% 30.30% 6.78% 25.78% 52.70%
## 1279: 19.26% 63.53% 30.30% 6.86% 25.63% 52.70%
## 1280: 19.22% 63.53% 30.30% 6.78% 25.63% 52.70%
## 1281: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1282: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1283: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1284: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1285: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1286: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1287: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1288: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1289: 19.42% 63.53% 30.51% 7.02% 25.78% 52.70%
## 1290: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1291: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1292: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1293: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1294: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1295: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1296: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1297: 19.30% 63.53% 30.08% 7.02% 25.63% 52.70%
## 1298: 19.26% 63.53% 30.08% 6.94% 25.63% 52.70%
## 1299: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1300: 19.30% 63.53% 30.08% 6.94% 25.78% 52.70%
## 1301: 19.34% 63.53% 30.08% 7.02% 25.78% 52.70%
## 1302: 19.34% 63.53% 30.08% 7.02% 25.63% 54.05%
## 1303: 19.38% 63.53% 30.08% 7.02% 25.78% 54.05%
## 1304: 19.38% 63.53% 30.08% 7.02% 25.78% 54.05%
## 1305: 19.38% 63.53% 30.08% 7.02% 25.78% 54.05%
## 1306: 19.42% 63.53% 30.30% 7.02% 25.78% 54.05%
## 1307: 19.42% 63.53% 30.30% 7.10% 25.78% 52.70%
## 1308: 19.42% 63.53% 30.30% 7.10% 25.78% 52.70%
## 1309: 19.38% 63.53% 30.08% 7.10% 25.63% 54.05%
## 1310: 19.38% 63.53% 29.87% 7.10% 25.78% 54.05%
## 1311: 19.46% 63.53% 30.51% 7.02% 25.78% 54.05%
## 1312: 19.46% 63.53% 30.30% 7.10% 25.78% 54.05%
## 1313: 19.42% 63.53% 30.08% 7.10% 25.78% 54.05%
## 1314: 19.46% 63.53% 30.30% 7.10% 25.78% 54.05%
## 1315: 19.49% 63.53% 30.51% 7.10% 25.78% 54.05%
## 1316: 19.42% 63.53% 30.30% 7.02% 25.78% 54.05%
## 1317: 19.42% 63.53% 30.30% 7.02% 25.78% 54.05%
## 1318: 19.38% 63.53% 30.30% 7.02% 25.63% 54.05%
## 1319: 19.38% 63.53% 30.51% 6.94% 25.63% 54.05%
## 1320: 19.42% 63.53% 30.51% 7.02% 25.63% 54.05%
## 1321: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1322: 19.49% 63.53% 30.51% 7.10% 25.78% 54.05%
## 1323: 19.42% 63.53% 30.30% 7.10% 25.63% 54.05%
## 1324: 19.42% 63.53% 30.30% 7.10% 25.63% 54.05%
## 1325: 19.38% 63.53% 30.30% 7.10% 25.48% 54.05%
## 1326: 19.42% 63.53% 30.30% 7.10% 25.63% 54.05%
## 1327: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1328: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1329: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1330: 19.42% 63.53% 30.51% 7.02% 25.63% 54.05%
## 1331: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1332: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1333: 19.49% 63.53% 30.51% 7.18% 25.63% 54.05%
## 1334: 19.42% 63.53% 30.51% 7.02% 25.63% 54.05%
## 1335: 19.53% 63.53% 30.72% 7.18% 25.63% 54.05%
## 1336: 19.53% 63.53% 30.72% 7.18% 25.63% 54.05%
## 1337: 19.49% 63.53% 30.51% 7.18% 25.63% 54.05%
## 1338: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1339: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1340: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1341: 19.34% 63.53% 30.51% 7.02% 25.34% 54.05%
## 1342: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1343: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1344: 19.46% 63.53% 30.72% 7.10% 25.48% 54.05%
## 1345: 19.38% 63.53% 30.51% 7.10% 25.34% 54.05%
## 1346: 19.49% 63.53% 30.72% 7.18% 25.48% 54.05%
## 1347: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1348: 19.42% 63.53% 30.51% 7.18% 25.34% 54.05%
## 1349: 19.42% 63.53% 30.72% 7.10% 25.34% 54.05%
## 1350: 19.38% 63.53% 30.51% 7.10% 25.34% 54.05%
## 1351: 19.38% 63.53% 30.51% 7.10% 25.34% 54.05%
## 1352: 19.38% 63.53% 30.51% 7.10% 25.34% 54.05%
## 1353: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1354: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1355: 19.53% 63.53% 30.72% 7.26% 25.48% 54.05%
## 1356: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1357: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1358: 19.46% 63.53% 30.72% 7.10% 25.48% 54.05%
## 1359: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1360: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1361: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1362: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1363: 19.49% 63.53% 30.72% 7.18% 25.48% 54.05%
## 1364: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1365: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1366: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1367: 19.34% 63.53% 30.30% 7.18% 25.34% 52.70%
## 1368: 19.38% 63.53% 30.30% 7.18% 25.48% 52.70%
## 1369: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1370: 19.42% 63.53% 30.08% 7.26% 25.48% 54.05%
## 1371: 19.46% 63.53% 30.30% 7.26% 25.48% 54.05%
## 1372: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1373: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1374: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1375: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1376: 19.49% 63.53% 30.51% 7.26% 25.48% 54.05%
## 1377: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1378: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1379: 19.34% 63.53% 29.87% 7.18% 25.48% 54.05%
## 1380: 19.34% 63.53% 29.87% 7.18% 25.48% 54.05%
## 1381: 19.34% 63.53% 29.87% 7.18% 25.48% 54.05%
## 1382: 19.38% 63.53% 30.08% 7.18% 25.48% 54.05%
## 1383: 19.34% 63.53% 29.87% 7.18% 25.48% 54.05%
## 1384: 19.38% 63.53% 30.08% 7.18% 25.48% 54.05%
## 1385: 19.42% 63.53% 30.08% 7.26% 25.48% 54.05%
## 1386: 19.46% 63.53% 30.30% 7.26% 25.48% 54.05%
## 1387: 19.34% 63.53% 30.30% 7.18% 25.19% 54.05%
## 1388: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1389: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1390: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1391: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1392: 19.42% 63.53% 30.51% 7.18% 25.34% 54.05%
## 1393: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1394: 19.30% 63.53% 30.08% 7.18% 25.19% 54.05%
## 1395: 19.30% 63.53% 30.08% 7.18% 25.19% 54.05%
## 1396: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1397: 19.26% 63.53% 30.08% 7.10% 25.19% 54.05%
## 1398: 19.34% 63.53% 30.30% 7.18% 25.19% 54.05%
## 1399: 19.30% 63.53% 30.30% 7.02% 25.34% 54.05%
## 1400: 19.30% 63.53% 30.08% 7.18% 25.19% 54.05%
## 1401: 19.22% 63.53% 30.08% 7.10% 25.04% 54.05%
## 1402: 19.30% 63.53% 30.08% 7.18% 25.19% 54.05%
## 1403: 19.34% 63.53% 30.08% 7.18% 25.34% 54.05%
## 1404: 19.30% 63.53% 30.30% 7.10% 25.19% 54.05%
## 1405: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1406: 19.42% 63.53% 30.30% 7.26% 25.34% 54.05%
## 1407: 19.26% 63.53% 30.08% 7.10% 25.19% 54.05%
## 1408: 19.34% 63.53% 30.08% 7.18% 25.34% 54.05%
## 1409: 19.34% 63.53% 30.08% 7.18% 25.34% 54.05%
## 1410: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1411: 19.30% 63.53% 30.08% 7.10% 25.34% 54.05%
## 1412: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1413: 19.34% 63.53% 30.08% 7.10% 25.48% 54.05%
## 1414: 19.42% 63.53% 30.08% 7.18% 25.63% 54.05%
## 1415: 19.38% 63.53% 30.30% 7.26% 25.19% 54.05%
## 1416: 19.38% 63.53% 30.51% 7.18% 25.19% 54.05%
## 1417: 19.34% 63.53% 30.51% 7.18% 25.04% 54.05%
## 1418: 19.38% 63.53% 30.72% 7.10% 25.19% 54.05%
## 1419: 19.49% 63.53% 30.72% 7.26% 25.34% 54.05%
## 1420: 19.38% 63.53% 30.30% 7.26% 25.19% 54.05%
## 1421: 19.34% 63.53% 30.30% 7.18% 25.19% 54.05%
## 1422: 19.18% 63.53% 30.08% 7.18% 24.74% 54.05%
## 1423: 19.26% 63.53% 30.08% 7.26% 24.89% 54.05%
## 1424: 19.30% 63.53% 30.08% 7.26% 25.04% 54.05%
## 1425: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1426: 19.30% 63.53% 30.08% 7.18% 25.19% 54.05%
## 1427: 19.22% 63.53% 29.87% 7.18% 25.04% 54.05%
## 1428: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1429: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1430: 19.22% 63.53% 29.87% 7.26% 24.89% 54.05%
## 1431: 19.30% 63.53% 30.08% 7.26% 25.04% 54.05%
## 1432: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1433: 19.30% 63.53% 30.08% 7.26% 25.04% 54.05%
## 1434: 19.30% 63.53% 29.87% 7.26% 25.19% 54.05%
## 1435: 19.34% 63.53% 30.08% 7.33% 25.04% 54.05%
## 1436: 19.30% 63.53% 29.87% 7.33% 25.04% 54.05%
## 1437: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1438: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1439: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1440: 19.42% 63.53% 30.08% 7.33% 25.34% 54.05%
## 1441: 19.42% 63.53% 30.08% 7.33% 25.34% 54.05%
## 1442: 19.30% 63.53% 30.08% 7.26% 25.04% 54.05%
## 1443: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1444: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1445: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1446: 19.42% 63.53% 30.08% 7.33% 25.34% 54.05%
## 1447: 19.42% 63.53% 30.08% 7.33% 25.34% 54.05%
## 1448: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1449: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1450: 19.34% 63.53% 29.87% 7.33% 25.19% 54.05%
## 1451: 19.42% 63.53% 30.30% 7.33% 25.19% 54.05%
## 1452: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1453: 19.46% 63.53% 30.30% 7.33% 25.34% 54.05%
## 1454: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1455: 19.49% 63.53% 30.30% 7.41% 25.34% 54.05%
## 1456: 19.38% 63.53% 30.30% 7.33% 25.04% 54.05%
## 1457: 19.34% 63.53% 30.08% 7.33% 25.04% 54.05%
## 1458: 19.34% 63.53% 30.08% 7.33% 25.04% 54.05%
## 1459: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1460: 19.34% 63.53% 30.08% 7.33% 25.04% 54.05%
## 1461: 19.30% 63.53% 30.08% 7.33% 25.04% 52.70%
## 1462: 19.42% 63.53% 30.08% 7.49% 25.04% 54.05%
## 1463: 19.42% 63.53% 30.30% 7.49% 25.04% 52.70%
## 1464: 19.42% 63.53% 30.30% 7.49% 25.04% 52.70%
## 1465: 19.46% 63.53% 30.30% 7.49% 25.04% 54.05%
## 1466: 19.34% 63.53% 30.08% 7.41% 25.04% 52.70%
## 1467: 19.38% 63.53% 30.08% 7.41% 25.04% 54.05%
## 1468: 19.38% 63.53% 30.08% 7.49% 25.04% 52.70%
## 1469: 19.38% 63.53% 30.08% 7.49% 25.04% 52.70%
## 1470: 19.38% 63.53% 30.08% 7.49% 25.04% 52.70%
## 1471: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1472: 19.34% 63.53% 30.08% 7.33% 25.19% 52.70%
## 1473: 19.30% 63.53% 29.87% 7.33% 25.19% 52.70%
## 1474: 19.34% 63.53% 30.08% 7.33% 25.19% 52.70%
## 1475: 19.26% 63.53% 29.87% 7.26% 25.19% 52.70%
## 1476: 19.38% 63.53% 30.08% 7.41% 25.19% 52.70%
## 1477: 19.34% 63.53% 30.08% 7.33% 25.19% 52.70%
## 1478: 19.26% 63.53% 29.87% 7.26% 25.19% 52.70%
## 1479: 19.26% 63.53% 29.87% 7.26% 25.19% 52.70%
## 1480: 19.34% 63.53% 30.08% 7.33% 25.19% 52.70%
## 1481: 19.30% 63.53% 29.87% 7.33% 25.19% 52.70%
## 1482: 19.30% 63.53% 29.87% 7.33% 25.19% 52.70%
## 1483: 19.34% 63.53% 29.87% 7.41% 25.19% 52.70%
## 1484: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1485: 19.30% 63.53% 29.87% 7.33% 25.19% 52.70%
## 1486: 19.38% 63.53% 29.87% 7.41% 25.34% 52.70%
## 1487: 19.30% 63.53% 29.87% 7.33% 25.19% 52.70%
## 1488: 19.38% 63.53% 29.87% 7.41% 25.34% 52.70%
## 1489: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1490: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1491: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1492: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1493: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1494: 19.38% 63.53% 29.87% 7.41% 25.34% 52.70%
## 1495: 19.38% 63.53% 29.87% 7.41% 25.34% 52.70%
## 1496: 19.42% 63.53% 30.08% 7.41% 25.34% 52.70%
## 1497: 19.38% 63.53% 29.87% 7.41% 25.34% 52.70%
## 1498: 19.38% 63.53% 29.66% 7.41% 25.48% 52.70%
## 1499: 19.42% 63.53% 29.87% 7.41% 25.48% 52.70%
## 1500: 19.38% 63.53% 29.66% 7.41% 25.48% 52.70%
test3 <- predict(model_rf3, newdata = test)
table(test3, test$Hospital.overall.rating)
##
## test3 1 2 3 4 5
## 1 13 6 0 0 0
## 2 19 139 14 0 0
## 3 0 67 468 62 0
## 4 0 0 22 231 23
## 5 0 0 0 0 14
summary(model_rf3)
## Length Class Mode
## call 8 -none- call
## type 1 -none- character
## predicted 2570 factor numeric
## err.rate 9000 -none- numeric
## confusion 30 -none- numeric
## votes 12850 matrix numeric
## oob.times 2570 -none- numeric
## classes 5 -none- character
## importance 53 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 2570 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms call
conf_matrix3 <- confusionMatrix(test3, test$Hospital.overall.rating, positive = "Yes")
conf_matrix3
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 13 6 0 0 0
## 2 19 139 14 0 0
## 3 0 67 468 62 0
## 4 0 0 22 231 23
## 5 0 0 0 0 14
##
## Overall Statistics
##
## Accuracy : 0.8024
## 95% CI : (0.7774, 0.8258)
## No Information Rate : 0.4675
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6909
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.40625 0.6557 0.9286 0.7884 0.37838
## Specificity 0.99426 0.9619 0.7753 0.9427 1.00000
## Pos Pred Value 0.68421 0.8081 0.7839 0.8370 1.00000
## Neg Pred Value 0.98206 0.9194 0.9252 0.9227 0.97838
## Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
## Detection Rate 0.01206 0.1289 0.4341 0.2143 0.01299
## Detection Prevalence 0.01763 0.1596 0.5538 0.2560 0.01299
## Balanced Accuracy 0.70026 0.8088 0.8519 0.8655 0.68919
# Confusion Matrix and Statistics
#
# Reference
# Prediction 1 2 3 4 5
# 1 13 6 0 0 0
# 2 19 139 14 0 0
# 3 0 67 468 62 0
# 4 0 0 22 231 23
# 5 0 0 0 0 14
#
# Overall Statistics
#
# Accuracy : 0.8024
# 95% CI : (0.7774, 0.8258)
# No Information Rate : 0.4675
# P-Value [Acc > NIR] : < 2.2e-16
#
# Kappa : 0.6909
# Mcnemar's Test P-Value : NA
#
# Statistics by Class:
#
# Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
# Sensitivity 0.40625 0.6557 0.9286 0.7884 0.37838
# Specificity 0.99426 0.9619 0.7753 0.9427 1.00000
# Pos Pred Value 0.68421 0.8081 0.7839 0.8370 1.00000
# Neg Pred Value 0.98206 0.9194 0.9252 0.9227 0.97838
# Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
# Detection Rate 0.01206 0.1289 0.4341 0.2143 0.01299
# Detection Prevalence 0.01763 0.1596 0.5538 0.2560 0.01299
# Balanced Accuracy 0.70026 0.8088 0.8519 0.8655 0.68919
# Accuracy: 80.24%
# We observe 80.24% accuracy with this model, all the classes have good specificity and sensitivity.
print(model_rf3)
##
## Call:
## randomForest(formula = Hospital.overall.rating ~ ., data = train, promiximity = FALSE, ntree = 1500, mtry = 20, do.trace = TRUE, na.action = na.omit)
## Type of random forest: classification
## Number of trees: 1500
## No. of variables tried at each split: 20
##
## OOB estimate of error rate: 19.38%
## Confusion matrix:
## 1 2 3 4 5 class.error
## 1 31 54 0 0 0 0.63529412
## 2 5 332 135 0 0 0.29661017
## 3 0 42 1174 52 0 0.07413249
## 4 0 0 169 500 2 0.25484352
## 5 0 0 0 39 35 0.52702703
# Call:
# randomForest(formula = Hospital.overall.rating ~ ., data = train, promiximity = FALSE, ntree = 1500, mtry = 20, do.trace = TRUE, na.action = na.omit)
# Type of random forest: classification
# Number of trees: 1500
# No. of variables tried at each split: 20
#
# OOB estimate of error rate: 19.38%
# Confusion matrix:
# 1 2 3 4 5 class.error
# 1 31 54 0 0 0 0.63529412
# 2 5 332 135 0 0 0.29661017
# 3 0 42 1174 52 0 0.07413249
# 4 0 0 169 500 2 0.25484352
# 5 0 0 0 39 35 0.52702703
# This plot provides us class errors.
plot(model_rf3)
plot(margin(model_rf3, test$Hospital.overall.rating))
# We see here most of the values >0 mean that the majority was right, hence this has predicted correct
# This plot provides us all the variables as per their importance in the model to predict the ratings.
varImpPlot(model_rf3)
# This provides the top 10 measures which are highly important as per the variance Importance measures.
varImpPlot(model_rf3, main = "Variable Importance of measures using MeanDecreaseGini", sort = T, n.var = 10)
# We have got a good accuracy for this model and as per this model the top 10 variables as per their importance are
# 1.READM_30_HOSP_WIDE 2.PSI_90_SAFETY 3.MORT_30_PN 4.H_HSP_RATING_LINEAR_MEAN 5.H_COMP_7_LINEAR_MEAN
# 6.H_RECMND_LINEAR_MEAN 7.H_COMP_3_LINEAR_MEAN 8.MORT_30_HF 9.H_COMP_1_LINEAR_MEAN 10.MORT_30_COPD
# Overall the model is doing good.
Data Modeling and Evaluation End
Factor Analysis
# install.packages("psych")
# install.packages("Information")
library(psych)
##
## Attaching package: 'psych'
## The following object is masked from 'package:randomForest':
##
## outlier
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
# As per the mentor suggestions we will first calculate the measures for effectiveness group and then create a function to perform the same for all the groups.
# effectiveness scores
effectiveness <- effe_master[, -c(2:8)]
str(effectiveness)
## 'data.frame': 4818 obs. of 19 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ CAC_3_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ IMM_2_score : num 0.364 0.53 0.613 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.233 -0.099 0.169 -2.11 -2.781 ...
## $ OP_22_score : num -1.201 -0.108 0.438 0.438 0.438 ...
## $ OP_23_score : num NA 0.783 NA NA NA ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.546 NA -1.232 NA ...
## $ PC_01_score : num 0.539 0.324 0.539 NA NA ...
## $ STK_4_score : num -1.23 NA NA NA NA ...
## $ STK_5_score : num -0.0249 0.1932 0.1932 0.4114 NA ...
## $ STK_6_score : num 0.446 -0.915 0.198 -3.019 NA ...
## $ STK_8_score : num -0.57 0.618 -0.296 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.381 -0.45 -1.282 0.381 NA ...
## $ VTE_3_score : num -0.278 0.819 -0.887 NA NA ...
## $ VTE_5_score : num -0.2536 0.6352 -0.0759 NA NA ...
## $ VTE_6_score : num 0.416 NA 0.416 NA NA ...
# remove the Provider.ID column
eff <- effectiveness[, -1]
str(eff)
## 'data.frame': 4818 obs. of 18 variables:
## $ CAC_3_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ IMM_2_score : num 0.364 0.53 0.613 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.233 -0.099 0.169 -2.11 -2.781 ...
## $ OP_22_score : num -1.201 -0.108 0.438 0.438 0.438 ...
## $ OP_23_score : num NA 0.783 NA NA NA ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.546 NA -1.232 NA ...
## $ PC_01_score : num 0.539 0.324 0.539 NA NA ...
## $ STK_4_score : num -1.23 NA NA NA NA ...
## $ STK_5_score : num -0.0249 0.1932 0.1932 0.4114 NA ...
## $ STK_6_score : num 0.446 -0.915 0.198 -3.019 NA ...
## $ STK_8_score : num -0.57 0.618 -0.296 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.381 -0.45 -1.282 0.381 NA ...
## $ VTE_3_score : num -0.278 0.819 -0.887 NA NA ...
## $ VTE_5_score : num -0.2536 0.6352 -0.0759 NA NA ...
## $ VTE_6_score : num 0.416 NA 0.416 NA NA ...
#scale the data
eff <- as.data.frame(scale(eff))
str(eff)
## 'data.frame': 4818 obs. of 18 variables:
## $ CAC_3_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ IMM_2_score : num 0.364 0.53 0.614 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.2335 -0.0993 0.1691 -2.1121 -2.783 ...
## $ OP_22_score : num -1.261 -0.117 0.455 0.455 0.455 ...
## $ OP_23_score : num NA 0.783 NA NA NA ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.547 NA -1.238 NA ...
## $ PC_01_score : num 0.568 0.339 0.568 NA NA ...
## $ STK_4_score : num -1.23 NA NA NA NA ...
## $ STK_5_score : num -0.0337 0.2084 0.2084 0.4505 NA ...
## $ STK_6_score : num 0.447 -0.921 0.199 -3.035 NA ...
## $ STK_8_score : num -0.573 0.62 -0.298 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.398 -0.481 -1.361 0.398 NA ...
## $ VTE_3_score : num -0.279 0.822 -0.892 NA NA ...
## $ VTE_5_score : num -0.2556 0.6375 -0.0769 NA NA ...
## $ VTE_6_score : num 0.469 NA 0.469 NA NA ...
# Using psych package, apply factor analysis on eff
# This command was provided by the mentor. We have to use the method=ML (maximum likelihood) and scores="tenBerge"
Eff.fa <- fa(eff, method = "ml", scores = "tenBerge")
# finding the weights
eff_weights <- Eff.fa$weights
eff_weights
## MR1
## CAC_3_score -0.042718102
## IMM_2_score 0.098638505
## IMM_3_OP_27_FAC_ADHPCT_score -0.002136510
## OP_22_score 0.069139536
## OP_23_score 0.026341891
## OP_29_score 0.002926853
## OP_30_score 0.043306208
## OP_4_score -0.061446640
## PC_01_score 0.012382577
## STK_4_score 0.045332087
## STK_5_score 0.032026744
## STK_6_score 0.252498944
## STK_8_score 0.158140046
## VTE_1_score 0.570885300
## VTE_2_score -0.003504823
## VTE_3_score 0.056708361
## VTE_5_score 0.041493877
## VTE_6_score 0.053009641
sum(eff_weights)
## [1] 1.353024
# [1] 1.353024
# The sum value is supposed to be 1
eff_weights <- eff_weights / sum(eff_weights)
sum(eff_weights)
## [1] 1
# CMS says a hospital needs to have at least 3 measures per group
# Rows with attribute values having more than 3 NA's are removed(Removing all such hospitals)
eff$remove <- 0
for (x in 1:nrow(eff)) {
if (sum(!is.na(eff[x,])) <= 3) { eff[x, c("remove")] = 1 }
else { eff[x, c("remove")] = 0 }
}
round(sum(eff$remove) / nrow(eff) * 100, 2)
## [1] 22.31
# [1] 22.31
# Thus, about 22% hospitals are not valid as per CMS restrictions.
row_num <- which(!eff$remove)
eff <- eff[which(!eff$remove),]
sum(eff$remove)
## [1] 0
# [1] 0
eff <- eff[, -ncol(eff)]
str(eff)
## 'data.frame': 3743 obs. of 18 variables:
## $ CAC_3_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ IMM_2_score : num 0.364 0.53 0.614 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.2335 -0.0993 0.1691 -2.1121 -2.783 ...
## $ OP_22_score : num -1.261 -0.117 0.455 0.455 0.455 ...
## $ OP_23_score : num NA 0.783 NA NA NA ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.547 NA -1.238 NA ...
## $ PC_01_score : num 0.568 0.339 0.568 NA NA ...
## $ STK_4_score : num -1.23 NA NA NA NA ...
## $ STK_5_score : num -0.0337 0.2084 0.2084 0.4505 NA ...
## $ STK_6_score : num 0.447 -0.921 0.199 -3.035 NA ...
## $ STK_8_score : num -0.573 0.62 -0.298 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.398 -0.481 -1.361 0.398 NA ...
## $ VTE_3_score : num -0.279 0.822 -0.892 NA NA ...
## $ VTE_5_score : num -0.2556 0.6375 -0.0769 NA NA ...
## $ VTE_6_score : num 0.469 NA 0.469 NA NA ...
# We will replace the NA's with the median values.
median_na <- function(x) {
x[which(is.na(x))] <- median(x, na.rm = T)
return(x)
}
eff <- data.frame(sapply(eff, median_na))
# calculating group scores
head(eff_weights)
## MR1
## CAC_3_score -0.031572305
## IMM_2_score 0.072902231
## IMM_3_OP_27_FAC_ADHPCT_score -0.001579062
## OP_22_score 0.051099988
## OP_23_score 0.019468894
## OP_29_score 0.002163193
# Multiply each attribute with corresponding weights obtained using Eff.fa$weights
eff <- eff * eff_weights
str(eff)
## 'data.frame': 3743 obs. of 18 variables:
## $ CAC_3_score : num -0.011709 0.027037 -0.000586 0.018951 0.00722 ...
## $ IMM_2_score : num 0.014259 -0.016747 0.044739 -0.000838 0.01009 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.00716 -0.00389 -0.00534 -0.15397 0.00439 ...
## $ OP_22_score : num -0.05284 -0.00359 0.01781 -0.01435 0.03314 ...
## $ OP_23_score : num -0.000464 0.032824 0.005498 0.007024 -0.00566 ...
## $ OP_29_score : num 0.155 -0.00179 -0.00426 -0.08056 0.03277 ...
## $ OP_30_score : num 0.008306 0.211053 -0.000851 -0.144511 0.02192 ...
## $ OP_4_score : num 0.06884 0.06398 0.15565 0.00321 0.01546 ...
## $ PC_01_score : num 0.01344 0.063335 0.066362 0.143197 -0.000879 ...
## $ STK_4_score : num -0.04113 0.00753 0.0594 0.0372 0.1343 ...
## $ STK_5_score : num -0.000308 0.006982 0.004933 0.084063 0.052649 ...
## $ STK_6_score : num -0.02032 -0.00842 0.00666 -0.07183 0.0603 ...
## $ STK_8_score : num -0.01834 -0.02818 -0.00272 0.01156 0.00817 ...
## $ VTE_1_score : num 0.000722 0.005707 -0.018689 0.003055 0.016393 ...
## $ VTE_2_score : num 0.00775 -0.00104 -0.04356 -0.01807 0.00364 ...
## $ VTE_3_score : num -0.01428 0.01601 -0.00193 0.01064 -0.0151 ...
## $ VTE_5_score : num 0.000404 0.032576 -0.001498 0.000993 0.014687 ...
## $ VTE_6_score : num 0.03418 -0.00074 0.02396 0.00913 0.00101 ...
# For each hospital, calculate average score for effectiveness
# Avg -> summed_score/number_of _measures
str(eff)
## 'data.frame': 3743 obs. of 18 variables:
## $ CAC_3_score : num -0.011709 0.027037 -0.000586 0.018951 0.00722 ...
## $ IMM_2_score : num 0.014259 -0.016747 0.044739 -0.000838 0.01009 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.00716 -0.00389 -0.00534 -0.15397 0.00439 ...
## $ OP_22_score : num -0.05284 -0.00359 0.01781 -0.01435 0.03314 ...
## $ OP_23_score : num -0.000464 0.032824 0.005498 0.007024 -0.00566 ...
## $ OP_29_score : num 0.155 -0.00179 -0.00426 -0.08056 0.03277 ...
## $ OP_30_score : num 0.008306 0.211053 -0.000851 -0.144511 0.02192 ...
## $ OP_4_score : num 0.06884 0.06398 0.15565 0.00321 0.01546 ...
## $ PC_01_score : num 0.01344 0.063335 0.066362 0.143197 -0.000879 ...
## $ STK_4_score : num -0.04113 0.00753 0.0594 0.0372 0.1343 ...
## $ STK_5_score : num -0.000308 0.006982 0.004933 0.084063 0.052649 ...
## $ STK_6_score : num -0.02032 -0.00842 0.00666 -0.07183 0.0603 ...
## $ STK_8_score : num -0.01834 -0.02818 -0.00272 0.01156 0.00817 ...
## $ VTE_1_score : num 0.000722 0.005707 -0.018689 0.003055 0.016393 ...
## $ VTE_2_score : num 0.00775 -0.00104 -0.04356 -0.01807 0.00364 ...
## $ VTE_3_score : num -0.01428 0.01601 -0.00193 0.01064 -0.0151 ...
## $ VTE_5_score : num 0.000404 0.032576 -0.001498 0.000993 0.014687 ...
## $ VTE_6_score : num 0.03418 -0.00074 0.02396 0.00913 0.00101 ...
eff = eff %>% mutate(score = round(rowSums(.) / length(eff), 3))
effectiveness <- effectiveness[row_num,]
effectiveness$score <- eff$score
effectiveness_scores <- effectiveness[, c("Provider.ID", "score")]
str(effectiveness_scores)
## 'data.frame': 3743 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ score : num 0.008 0.022 0.017 -0.009 0.022 -0.088 0.009 0.023 -0.148 -0.01 ...
# let us rename the column with appropriate measure name.
names(effectiveness_scores)[2] <- paste("effe_score")
str(effectiveness_scores)
## 'data.frame': 3743 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ effe_score : num 0.008 0.022 0.017 -0.009 0.022 -0.088 0.009 0.023 -0.148 -0.01 ...
# Since we need to repeat this process for the rest of the measures let us create a function
function_group_score <- function(data) {
# remove the provide.ID column
df <- data[, -1]
#scale the data
df <- as.data.frame(scale(df))
str(df)
# Using psych package, apply factor analysis on data
# This command was provided by the mentor. We have to use the method=ML (maximum likelihood) and scores="tenBerge"
df.fa <- fa(df, method = "ml", scores = "tenBerge")
# finding the weights
df_weights <- df.fa$weights
sum(df_weights)
df_weights <- df_weights / sum(df_weights)
sum(df_weights)
# CMS says a hospital needs to have at least 3 measures per group
# Rows with attribute values having more than 3 NA's are removed(Removing all such hospitals)
df$remove <- 0
for (i in 1:nrow(df)) {
if (sum(!is.na(df[i,])) <= 3) { df[i, c("remove")] = 1 }
else { df[i, c("remove")] = 0 }
}
row_num <- which(!df$remove)
df <- df[which(!df$remove),]
sum(df$remove)
df <- df[, -ncol(df)]
str(df)
# We will replace the NA's with the median values.
median_na <- function(x) {
x[which(is.na(x))] <- median(x, na.rm = T)
return(x)
}
df <- data.frame(sapply(df, median_na))
# calculating group scores
# Multiply each attribute with corresponding weights obtained using df.fa$weights
df <- df * df_weights
# For each hospital, calculate average score for effectiveness
# Avg -> summed_score/number_of _measures
df = df %>% mutate(score = round(rowSums(.) / length(df), 3))
data <- data[row_num,]
data$score <- df$score
data_scores <- data[, c("Provider.ID", "score")]
# let us rename the column with appropriate measure name.
names(data_scores)[2] <- paste("df_score")
return(data_scores)
}
# readmission scores
data <- read_master[, -c(2:8)]
readmission_scores <- function_group_score(data)
## 'data.frame': 4818 obs. of 8 variables:
## $ READM_30_AMI_score : num 0.41 0.201 0.829 NA NA ...
## $ READM_30_CABG_score : num -0.619 NA -0.708 NA NA ...
## $ READM_30_COPD_score : num -0.8701 1.5792 0.1571 0.0781 0.6311 ...
## $ READM_30_HF_score : num 0.372 0.036 0.909 0.573 -0.77 ...
## $ READM_30_HIP_KNEE_score : num -0.889 -1.979 -0.708 NA NA ...
## $ READM_30_HOSP_WIDE_score: num 0.218 0.829 0.218 -1.248 -0.148 ...
## $ READM_30_PN_score : num -1.111 0.497 -0.551 -0.132 0.776 ...
## $ READM_30_STK_score : num -0.125 -0.779 0.529 -0.125 NA ...
## 'data.frame': 3816 obs. of 8 variables:
## $ READM_30_AMI_score : num 0.41 0.201 0.829 NA NA ...
## $ READM_30_CABG_score : num -0.619 NA -0.708 NA NA ...
## $ READM_30_COPD_score : num -0.8701 1.5792 0.1571 0.0781 0.6311 ...
## $ READM_30_HF_score : num 0.372 0.036 0.909 0.573 -0.77 ...
## $ READM_30_HIP_KNEE_score : num -0.889 -1.979 -0.708 NA NA ...
## $ READM_30_HOSP_WIDE_score: num 0.218 0.829 0.218 -1.248 -0.148 ...
## $ READM_30_PN_score : num -1.111 0.497 -0.551 -0.132 0.776 ...
## $ READM_30_STK_score : num -0.125 -0.779 0.529 -0.125 NA ...
str(readmission_scores)
## 'data.frame': 3816 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10019 10021 ...
## $ df_score : num -0.04 0.002 0.006 -0.01 0.002 0.069 -0.013 0.004 -0.056 0.007 ...
names(readmission_scores)[2] <- "radm_score"
head(readmission_scores)
## Provider.ID radm_score
## 1 10001 -0.040
## 2 10005 0.002
## 3 10006 0.006
## 4 10007 -0.010
## 5 10008 0.002
## 6 10011 0.069
# mortality scores
data <- mort_master[, -c(2:8)]
mortality_scores <- function_group_score(data)
## 'data.frame': 4818 obs. of 7 variables:
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.11 NA NA ...
## $ MORT_30_CABG_score : num -1.01 NA -0.89 NA NA ...
## $ MORT_30_COPD_score : num -1.099 0.436 0.887 -1.099 -0.106 ...
## $ MORT_30_HF_score : num -0.166 -2.294 -2.362 -1.539 -0.372 ...
## $ MORT_30_PN_score : num 0.429 -2.114 -0.866 -1.154 0.333 ...
## $ MORT_30_STK_score : num -0.283 -0.343 -1.79 -1.007 NA ...
## $ PSI_4_SURG_COMP_score: num -1.72 -2.3 -3.34 NA NA ...
## 'data.frame': 3480 obs. of 7 variables:
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.11 NA NA ...
## $ MORT_30_CABG_score : num -1.01 NA -0.89 NA NA ...
## $ MORT_30_COPD_score : num -1.099 0.436 0.887 -1.099 -0.106 ...
## $ MORT_30_HF_score : num -0.166 -2.294 -2.362 -1.539 -0.372 ...
## $ MORT_30_PN_score : num 0.429 -2.114 -0.866 -1.154 0.333 ...
## $ MORT_30_STK_score : num -0.283 -0.343 -1.79 -1.007 NA ...
## $ PSI_4_SURG_COMP_score: num -1.72 -2.3 -3.34 NA NA ...
str(mortality_scores)
## 'data.frame': 3480 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10019 10021 ...
## $ df_score : num -0.021 -0.164 -0.182 -0.066 0.003 -0.009 -0.162 -0.069 -0.069 0.037 ...
names(mortality_scores)[2] <- "mort_score"
head(mortality_scores)
## Provider.ID mort_score
## 1 10001 -0.021
## 2 10005 -0.164
## 3 10006 -0.182
## 4 10007 -0.066
## 5 10008 0.003
## 6 10011 -0.009
# safety scores
data <- safe_master[, -c(2:8)]
safety_scores <- function_group_score(data)
## 'data.frame': 4818 obs. of 8 variables:
## $ COMP_HIP_KNEE_score: num -1.3622 0.0741 -1.3622 NA NA ...
## $ HAI_1_SIR_score : num -2.385 -1.038 0.393 NA NA ...
## $ HAI_2_SIR_score : num -2.1956 0.0472 -0.3802 1.1004 NA ...
## $ HAI_3_SIR_score : num -1.139 0.724 0.82 NA NA ...
## $ HAI_4_SIR_score : num 1.02 NA NA NA NA ...
## $ HAI_5_SIR_score : num 0.65 -0.461 -0.315 NA NA ...
## $ HAI_6_SIR_score : num 0.0559 0.8017 0.5908 1.5924 0.4503 ...
## $ PSI_90_SAFETY_score: num 1.2198 0.2314 -0.1175 0.5802 -0.0593 ...
## 'data.frame': 2954 obs. of 8 variables:
## $ COMP_HIP_KNEE_score: num -1.3622 0.0741 -1.3622 NA -1.1826 ...
## $ HAI_1_SIR_score : num -2.3846 -1.0384 0.3931 NA -0.0358 ...
## $ HAI_2_SIR_score : num -2.1956 0.0472 -0.3802 1.1004 0.188 ...
## $ HAI_3_SIR_score : num -1.139 0.724 0.82 NA -0.584 ...
## $ HAI_4_SIR_score : num 1.02 NA NA NA 1.02 ...
## $ HAI_5_SIR_score : num 0.65 -0.461 -0.315 NA 0.118 ...
## $ HAI_6_SIR_score : num 0.0559 0.8017 0.5908 1.5924 0.2258 ...
## $ PSI_90_SAFETY_score: num 1.22 0.231 -0.117 0.58 -0.583 ...
str(safety_scores)
## 'data.frame': 2954 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10011 10012 10016 10019 10021 10023 ...
## $ df_score : num -0.104 -0.005 -0.047 0.051 -0.052 0.06 0.032 0.033 0.04 -0.014 ...
names(safety_scores)[2] <- "safety_score"
str(safety_scores)
## 'data.frame': 2954 obs. of 2 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10011 10012 10016 10019 10021 10023 ...
## $ safety_score: num -0.104 -0.005 -0.047 0.051 -0.052 0.06 0.032 0.033 0.04 -0.014 ...
# experience scores
data <- expe_master[, -c(2:8)]
experience_scores <- function_group_score(data)
## 'data.frame': 4818 obs. of 11 variables:
## $ H_CLEAN_LINEAR_SCORE : num -0.855 -1.114 -1.114 0.442 NA ...
## $ H_COMP_1_LINEAR_SCORE : num -0.528 -0.131 -0.131 -0.131 NA ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0404 0.8652 0.8652 1.6899 NA ...
## $ H_COMP_3_LINEAR_SCORE : num -1.203 -0.291 -0.519 0.393 NA ...
## $ H_COMP_4_LINEAR_SCORE : num -0.611 0.168 -0.221 0.557 NA ...
## $ H_COMP_5_LINEAR_SCORE : num -0.417 0.285 -0.183 0.753 NA ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0273 0.3106 -1.1058 -0.256 NA ...
## $ H_COMP_7_LINEAR_SCORE : num 0.166 -0.186 -0.537 0.166 NA ...
## $ H_HSP_RATING_LINEAR_SCORE: num 0.0837 0.3939 -1.1573 -0.5368 NA ...
## $ H_QUIET_LINEAR_SCORE : num 0.97 0.578 0.578 1.755 NA ...
## $ H_RECMND_LINEAR_SCORE : num 0.452 0.222 -0.932 -0.47 NA ...
## 'data.frame': 3508 obs. of 11 variables:
## $ H_CLEAN_LINEAR_SCORE : num -0.855 -1.114 -1.114 0.442 -1.633 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.528 -0.131 -0.131 -0.131 -0.528 ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0404 0.8652 0.8652 1.6899 0.0404 ...
## $ H_COMP_3_LINEAR_SCORE : num -1.203 -0.291 -0.519 0.393 -0.747 ...
## $ H_COMP_4_LINEAR_SCORE : num -0.611 0.168 -0.221 0.557 -0.611 ...
## $ H_COMP_5_LINEAR_SCORE : num -0.417 0.285 -0.183 0.753 -1.119 ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0273 0.3106 -1.1058 -0.256 -0.256 ...
## $ H_COMP_7_LINEAR_SCORE : num 0.166 -0.186 -0.537 0.166 -0.186 ...
## $ H_HSP_RATING_LINEAR_SCORE: num 0.0837 0.3939 -1.1573 -0.5368 -0.2266 ...
## $ H_QUIET_LINEAR_SCORE : num 0.97 0.578 0.578 1.755 0.186 ...
## $ H_RECMND_LINEAR_SCORE : num 0.452 0.222 -0.932 -0.47 -0.24 ...
str(experience_scores)
## 'data.frame': 3508 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10011 10012 10016 10019 10021 10022 ...
## $ df_score : num -0.033 -0.017 -0.039 0.039 -0.03 0.017 -0.007 -0.021 0.023 0.069 ...
names(experience_scores)[2] <- "expe_score"
str(experience_scores)
## 'data.frame': 3508 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10011 10012 10016 10019 10021 10022 ...
## $ expe_score : num -0.033 -0.017 -0.039 0.039 -0.03 0.017 -0.007 -0.021 0.023 0.069 ...
# medical scores
data <- medi_master[, -c(2:8)]
medical_scores <- function_group_score(data)
## 'data.frame': 4818 obs. of 5 variables:
## $ OP_10_score: num 0.251 -0.425 -0.278 -1.503 0.526 ...
## $ OP_11_score: num 0.391 -1.217 -0.248 -0.508 NA ...
## $ OP_13_score: num -1.207 -0.308 2.339 NA NA ...
## $ OP_14_score: num 0.208 -0.651 -0.973 NA 1.174 ...
## $ OP_8_score : num 0.301 -0.379 -0.784 NA NA ...
## 'data.frame': 2792 obs. of 5 variables:
## $ OP_10_score: num 0.251 -0.425 -0.278 0.643 0.28 ...
## $ OP_11_score: num 0.391 -1.217 -0.248 -1.251 -1.32 ...
## $ OP_13_score: num -1.207 -0.308 2.339 NA 0.791 ...
## $ OP_14_score: num 0.208 -0.651 -0.973 -0.329 NA ...
## $ OP_8_score : num 0.301 -0.379 -0.784 0.576 NA ...
str(medical_scores)
## 'data.frame': 2792 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ df_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
names(medical_scores)[2] <- "medi_score"
str(medical_scores)
## 'data.frame': 2792 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
# timeliness scores
data <- time_master[, -c(2:8)]
timeliness_scores <- function_group_score(data)
## 'data.frame': 4818 obs. of 7 variables:
## $ ED_1b_score : num 0.0889 0.3434 0.5979 0.5783 0.9601 ...
## $ ED_2b_score : num 0.51 0.464 0.358 0.51 0.693 ...
## $ OP_18b_score: num -1.279 0.62 0.235 0.572 1.077 ...
## $ OP_20_score : num -2.4285 -0.0502 1.0138 -0.7387 -0.0502 ...
## $ OP_21_score : num -2.592 -0.381 -0.267 -2.082 0.243 ...
## $ OP_3b_score : num NA NA NA NA NA ...
## $ OP_5_score : num NA -0.727 NA 0.25 NA ...
## 'data.frame': 3531 obs. of 7 variables:
## $ ED_1b_score : num 0.0889 0.3434 0.5979 0.5783 0.9601 ...
## $ ED_2b_score : num 0.51 0.464 0.358 0.51 0.693 ...
## $ OP_18b_score: num -1.279 0.62 0.235 0.572 1.077 ...
## $ OP_20_score : num -2.4285 -0.0502 1.0138 -0.7387 -0.0502 ...
## $ OP_21_score : num -2.592 -0.381 -0.267 -2.082 0.243 ...
## $ OP_3b_score : num NA NA NA NA NA ...
## $ OP_5_score : num NA -0.727 NA 0.25 NA ...
str(timeliness_scores)
## 'data.frame': 3531 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ df_score : num -0.043 0.071 -0.006 0.111 0.107 -0.229 0.075 -0.048 0.08 -0.042 ...
names(timeliness_scores)[2] <- "time_score"
str(timeliness_scores)
## 'data.frame': 3531 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ time_score : num -0.043 0.071 -0.006 0.111 0.107 -0.229 0.075 -0.048 0.08 -0.042 ...
# Merge all the group scores into a master data frame group_scores using providerID
merge1 = merge(readmission_scores, mortality_scores, by = "Provider.ID")
merge2 = merge(merge1, safety_scores, by = "Provider.ID")
merge3 = merge(merge2, experience_scores, by = "Provider.ID")
merge4 = merge(merge3, medical_scores, by = "Provider.ID")
merge5 = merge(merge4, timeliness_scores, by = "Provider.ID")
merge6 = merge(merge5, effectiveness_scores, by = "Provider.ID")
group_scores <- merge6
##########################################3############ group scores calculation complete ###############################################################
calculating the final scores
str(group_scores)
## 'data.frame': 2414 obs. of 8 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score: num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
cms_weights <- c(readmission = 0.22, mortality = 0.22, safety = 0.22, experience = 0.22, medical = 0.04, timeliness = 0.04, effectiveness = 0.04)
group_scores[, "final_score"] <-
apply(group_scores[, -c(1, ncol(group_scores))], 1, function(x)
round(sum(x * cms_weights, na.rm = T), 3))
## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
str(group_scores)
## 'data.frame': 2414 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score: num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
head(group_scores)
## Provider.ID radm_score mort_score safety_score expe_score medi_score
## 1 10001 -0.040 -0.021 -0.104 -0.033 0.033
## 2 10005 0.002 -0.164 -0.005 -0.017 -0.070
## 3 10006 0.006 -0.182 -0.047 -0.039 0.137
## 4 10011 0.069 -0.009 -0.052 -0.030 -0.080
## 5 10012 -0.013 -0.162 0.060 0.017 -0.112
## 6 10016 0.004 -0.069 0.032 -0.007 -0.271
## time_score effe_score final_score
## 1 -0.043 0.008 -0.046
## 2 0.071 0.022 -0.040
## 3 -0.006 0.017 -0.052
## 4 -0.229 -0.088 -0.014
## 5 0.075 0.009 -0.024
## 6 -0.048 0.023 -0.021
# > head(group_scores)
# Provider.ID radm_score mort_score safety_score expe_score medi_score time_score effe_score final_score
# 1 10001 -0.040 -0.021 -0.104 -0.033 0.033 -0.043 0.008 -0.046
# 2 10005 0.002 -0.164 -0.005 -0.017 -0.070 0.071 0.022 -0.040
# 3 10006 0.006 -0.182 -0.047 -0.039 0.137 -0.006 0.017 -0.052
# 4 10011 0.069 -0.009 -0.052 -0.030 -0.080 -0.229 -0.088 -0.014
# 5 10012 -0.013 -0.162 0.060 0.017 -0.112 0.075 0.009 -0.024
# 6 10016 0.004 -0.069 0.032 -0.007 -0.271 -0.048 0.023 -0.021
# >
final_score_df <- group_scores[, c(1, ncol(group_scores))]
summary(final_score_df$final_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.261000 -0.025000 0.000000 -0.002328 0.024000 0.250000
# > summary(final_score_df$final_score)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# -0.261000 -0.025000 0.000000 -0.002328 0.024000 0.250000
# Removing the final_score column from group_scores
str(group_scores)
## 'data.frame': 2414 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score: num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
group_scores <- group_scores[, -9]
calculating the final scores complete
Unsupervised Modelling using - kmeans clustering
# The values for nstart and ncluster are as per the mentor's suggestions.
score <- kmeans(final_score_df$final_score, 5, nstart = 100)
summary(score)
## Length Class Mode
## cluster 2414 -none- numeric
## centers 5 -none- numeric
## totss 1 -none- numeric
## withinss 5 -none- numeric
## tot.withinss 1 -none- numeric
## betweenss 1 -none- numeric
## size 5 -none- numeric
## iter 1 -none- numeric
## ifault 1 -none- numeric
summary(factor(score$cluster))
## 1 2 3 4 5
## 46 114 421 849 984
str(score)
## List of 9
## $ cluster : int [1:2414] 3 3 3 5 5 5 5 4 3 5 ...
## $ centers : num [1:5, 1] -0.14433 0.09054 -0.05661 0.02868 -0.00998
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:5] "1" "2" "3" "4" ...
## .. ..$ : NULL
## $ totss : num 4.65
## $ withinss : num [1:5] 0.0824 0.1436 0.1175 0.141 0.1412
## $ tot.withinss: num 0.626
## $ betweenss : num 4.02
## $ size : int [1:5] 46 114 421 849 984
## $ iter : int 2
## $ ifault : int 0
## - attr(*, "class")= chr "kmeans"
final_score_df$cluster_id <- score$cluster
f = final_score_df %>%
group_by(cluster_id) %>%
summarise(avg_score = mean(final_score)) %>%
arrange(desc(avg_score))
f
## # A tibble: 5 x 2
## cluster_id avg_score
## <int> <dbl>
## 1 2 0.0905
## 2 4 0.0287
## 3 5 -0.00998
## 4 3 -0.0566
## 5 1 -0.144
# We notice that after arranging the avg_score column in descending order, the cluster_id values are different.
# we now need to reassign the cluster ratings according to the average rating
# The top most one should be as follows, 5, 4, 3, 2, 1
# We will adjust this by creating a new clusterid column and assign the values to it.
str(f)
## Classes 'tbl_df', 'tbl' and 'data.frame': 5 obs. of 2 variables:
## $ cluster_id: int 2 4 5 3 1
## $ avg_score : num 0.09054 0.02868 -0.00998 -0.05661 -0.14433
final_score_df$newcluster_id <-
if_else(
final_score_df$cluster_id == f$cluster_id[1],
5,
if_else(
final_score_df$cluster_id == f$cluster_id[2],
4,
if_else(
final_score_df$cluster_id == f$cluster_id[3],
3,
if_else(final_score_df$cluster_id == f$cluster_id[4], 2, 1)
)
)
)
final_score_df %>%
group_by(newcluster_id) %>%
summarise(avg_score = mean(final_score)) %>%
arrange(desc(avg_score))
## # A tibble: 5 x 2
## newcluster_id avg_score
## <dbl> <dbl>
## 1 5 0.0905
## 2 4 0.0287
## 3 3 -0.00998
## 4 2 -0.0566
## 5 1 -0.144
# # A tibble: 5 x 2
# cluster_id avg_score
# <int> <dbl>
# 5 0.0905
# 4 0.0287
# 3 -0.00972
# 2 -0.0558
# 1 -0.142
str(final_score_df)
## 'data.frame': 2414 obs. of 4 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: num 2 2 2 3 3 3 3 4 2 3 ...
final_score_df$newcluster_id <- as.factor(final_score_df$newcluster_id)
summary(final_score_df$newcluster_id)
## 1 2 3 4 5
## 46 421 984 849 114
# > summary(final_score_df$newcluster_id)
# 1 2 3 4 5
# 48 430 973 849 114
# We see that there are around 114 providers who have top 5 rating and as usual most of the providers are in the median 3 rating.
# Around 849 providers are in top 4 rating.
summary(final_score_df$final_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.261000 -0.025000 0.000000 -0.002328 0.024000 0.250000
# Visualising the final_score accross all the ratings
head(final_score_df)
## Provider.ID final_score cluster_id newcluster_id
## 1 10001 -0.046 3 2
## 2 10005 -0.040 3 2
## 3 10006 -0.052 3 2
## 4 10011 -0.014 5 3
## 5 10012 -0.024 5 3
## 6 10016 -0.021 5 3
final_plot <- ggplot(final_score_df, aes(x = newcluster_id, y = final_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Final Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.2, 0.2, 0.05))
final_plot
# Let us plot each group_scores against the final_score_df
# Merging the group_scores with final_scores_df
all_scores <- merge(group_scores, final_score_df, by = "Provider.ID")
# all_scores group by readmission
summary(all_scores$radm_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.8400000 -0.0310000 0.0015000 -0.0003761 0.0340000 0.8150000
readm_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = radm_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Readmission Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-1.0, 1.0, 0.25))
readm_plot
# all_scores group by mortality
summary(all_scores$mort_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.280000 -0.047000 0.004000 0.003586 0.055750 0.330000
mort_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = mort_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Mortality Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.2, 0.3, 0.05))
mort_plot
# all_scores group by safety
summary(all_scores$safety_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.3210000 -0.0310000 0.0070000 -0.0003123 0.0380000 0.1510000
safe_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = safety_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("safety scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.3, 0.2, 0.04))
safe_plot
# all_scores group by experience
summary(all_scores$expe_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.37200 -0.04900 -0.00700 -0.01326 0.03100 0.21200
expe_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = expe_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Experience Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.4, 0.25, 0.05))
expe_plot
# all_scores group by medical
summary(all_scores$medi_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.664000 -0.042000 0.030000 0.007466 0.073000 0.369000
medi_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = medi_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Medical Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.5, 0.2, 0.05))
medi_plot
# all_scores group by timeliness
summary(all_scores$time_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.938000 -0.053000 0.016000 -0.008087 0.060000 0.284000
time_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = time_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Timeliness Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.9, 0.3, 0.08))
time_plot
# all_scores group by effectiveness
summary(all_scores$effe_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.15300 0.00000 0.01400 0.00808 0.02300 0.05300
effe_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = effe_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Effectiveness Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.2, 0.1, 0.01))
effe_plot
grid_plot2 <- plot_grid(readm_plot, mort_plot, safe_plot, expe_plot, medi_plot, time_plot, effe_plot,
labels = c("Readmission", "Mortality", "Safety", "Experience", "Medical", "Timeliness", "Effectiveness"))
grid_plot2
# Now let us compare the median of all the groups scores with the ratings
median_group_scores <- all_scores[, -c(1, 10)] %>%
group_by(newcluster_id) %>%
summarise_all(.funs = (median), na.rm = T)
median_group_scores
## # A tibble: 5 x 9
## newcluster_id radm_score mort_score safety_score expe_score medi_score
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 -0.353 -0.0405 -0.052 -0.086 0.0155
## 2 2 -0.048 -0.046 -0.031 -0.063 0.006
## 3 3 -0.006 -0.008 0.001 -0.021 0.02
## 4 4 0.024 0.03 0.027 0.023 0.044
## 5 5 0.174 0.066 0.0415 0.037 0.056
## # … with 3 more variables: time_score <dbl>, effe_score <dbl>,
## # final_score <dbl>
# A tibble: 5 x 8
# newcluster_id radm_score mort_score safety_score expe_score medi_score time_score effe_score final_score
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -0.353 -0.0405 -0.052 -0.086 0.0155 -0.0695 0.011 -0.126
# 2 -0.048 -0.046 -0.031 -0.063 0.006 -0.038 0.012 -0.053
# 3 -0.006 -0.008 0.001 -0.021 0.02 0.0165 0.013 -0.009
# 4 0.024 0.03 0.027 0.023 0.044 0.029 0.016 0.027
# 5 0.174 0.066 0.0415 0.037 0.056 0.0365 0.019 0.077
# > >
# We can clearly see the rating increases as the measure scores increase.
mean_group_scores <- all_scores[, -c(1, 10)] %>%
group_by(newcluster_id) %>%
summarise_all(.funs = (mean), na.rm = T)
mean_group_scores
## # A tibble: 5 x 9
## newcluster_id radm_score mort_score safety_score expe_score medi_score
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 -0.356 -0.0473 -0.0641 -0.100 -0.0304
## 2 2 -0.0706 -0.0475 -0.0402 -0.0698 -0.0257
## 3 3 -0.0100 -0.00655 -0.00340 -0.0223 -0.000317
## 4 4 0.0353 0.0352 0.0224 0.0222 0.0302
## 5 5 0.220 0.0650 0.0300 0.0449 0.0432
## # … with 3 more variables: time_score <dbl>, effe_score <dbl>,
## # final_score <dbl>
# newcluster_id radm_score mort_score safety_score expe_score medi_score time_score effe_score final_score
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -0.342 -0.0501 -0.0654 -0.103 -0.0380 -0.104 -0.00231 -0.142
# 2 -0.0711 -0.0455 -0.0393 -0.0689 -0.0244 -0.0621 0.00560 -0.0558
# 3 -0.00925 -0.00680 -0.00325 -0.0220 -0.000215 -0.00609 0.00622 -0.00972
# 4 0.0353 0.0352 0.0224 0.0222 0.0302 0.0186 0.0109 0.0287
# 5 0.220 0.0650 0.0300 0.0449 0.0432 0.0199 0.0170 0.0905
# We can clearly see the rating increases as the measure scores increase.
Validation of Star Ratings
# master_data_y is the hospital_ratings dataset we will use it here.
str(master_data_y)
## 'data.frame': 4818 obs. of 2 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.overall.rating: int 3 3 2 3 3 2 3 3 NA 2 ...
# Let us convert the ratings column to factor
master_data_y$Hospital.overall.rating <- factor(master_data_y$Hospital.overall.rating)
# merging the final_score_df with the master_data_y using Provider.ID
final_score_df <- merge(final_score_df, master_data_y, by = "Provider.ID")
str(final_score_df)
## 'data.frame': 2414 obs. of 5 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id : Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
## $ Hospital.overall.rating: Factor w/ 5 levels "1","2","3","4",..: 3 3 2 2 3 3 2 4 3 3 ...
summary(final_score_df$newcluster_id)
## 1 2 3 4 5
## 46 421 984 849 114
# Accuracy is determined by comparing the overall ratings of CMS(general.csv) by creating the confusion matrix.
final = table(final_score_df$newcluster_id, final_score_df$Hospital.overall.rating)
final
##
## 1 2 3 4 5
## 1 21 21 2 2 0
## 2 78 248 89 6 0
## 3 2 249 622 110 1
## 4 0 19 377 422 31
## 5 0 1 14 73 26
conf_matrix4 <- confusionMatrix(final)
conf_matrix4
## Confusion Matrix and Statistics
##
##
## 1 2 3 4 5
## 1 21 21 2 2 0
## 2 78 248 89 6 0
## 3 2 249 622 110 1
## 4 0 19 377 422 31
## 5 0 1 14 73 26
##
## Overall Statistics
##
## Accuracy : 0.5547
## 95% CI : (0.5346, 0.5746)
## No Information Rate : 0.4573
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3484
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.207921 0.4610 0.5634 0.6884 0.44828
## Specificity 0.989192 0.9078 0.7237 0.7629 0.96265
## Pos Pred Value 0.456522 0.5891 0.6321 0.4971 0.22807
## Neg Pred Value 0.966216 0.8545 0.6629 0.8780 0.98609
## Prevalence 0.041839 0.2229 0.4573 0.2539 0.02403
## Detection Rate 0.008699 0.1027 0.2577 0.1748 0.01077
## Detection Prevalence 0.019056 0.1744 0.4076 0.3517 0.04722
## Balanced Accuracy 0.598556 0.6844 0.6435 0.7257 0.70546
# Confusion Matrix and Statistics
#
#
# 1 2 3 4 5
# 1 22 22 2 2 0
# 2 77 253 93 7 0
# 3 2 243 618 109 1
# 4 0 19 377 422 31
# 5 0 1 14 73 26
#
# Overall Statistics
#
# Accuracy : 0.5555
# 95% CI : (0.5354, 0.5755)
# No Information Rate : 0.4573
# P-Value [Acc > NIR] : < 2.2e-16
#
# Kappa : 0.3508
# Mcnemar's Test P-Value : NA
#
# Statistics by Class:
#
# Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
# Sensitivity 0.217822 0.4703 0.5598 0.6884 0.44828
# Specificity 0.988759 0.9057 0.7290 0.7629 0.96265
# Pos Pred Value 0.458333 0.5884 0.6351 0.4971 0.22807
# Neg Pred Value 0.966610 0.8564 0.6627 0.8780 0.98609
# Prevalence 0.041839 0.2229 0.4573 0.2539 0.02403
# Detection Rate 0.009114 0.1048 0.2560 0.1748 0.01077
# Detection Prevalence 0.019884 0.1781 0.4031 0.3517 0.04722
# Balanced Accuracy 0.603290 0.6880 0.6444 0.7257 0.70546
# Accuracy : 55.55%
# We see the accuracy is between 50 to 60%, and the model has good scope as it is not overfitting the data.
End of Factor Analysis and Un-supervised Clustering
Provider Analysis for Evanston Hospital
# Using the group_scores dataset
# all_scores
str(all_scores)
## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
# Provider: Evanston Hospital Provider.ID = 140010
# Check the group scores for the provider.ID = 140010
provider_140010_gp_scores <- all_scores[which(all_scores$Provider.ID == 140010),]
provider_140010_gp_scores
## Provider.ID radm_score mort_score safety_score expe_score medi_score
## 637 140010 0.019 0.278 -0.022 -0.002 -0.013
## time_score effe_score final_score cluster_id newcluster_id
## 637 0.068 0.015 0.063 2 5
# Provider.ID radm_score mort_score safety_score expe_score medi_score time_score effe_score final_score cluster_id newcluster_id
# 637 140010 0.019 0.278 -0.022 -0.002 -0.013 0.068 0.015 0.063 3 5
str(master_data_y)
## 'data.frame': 4818 obs. of 2 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.overall.rating: Factor w/ 5 levels "1","2","3","4",..: 3 3 2 3 3 2 3 3 NA 2 ...
provider_140010_cms_rating = master_data_y[which(master_data_y$Provider.ID == 140010),]
str(provider_140010_cms_rating)
## 'data.frame': 1 obs. of 2 variables:
## $ Provider.ID : int 140010
## $ Hospital.overall.rating: Factor w/ 5 levels "1","2","3","4",..: 3
as.numeric(provider_140010_cms_rating$Hospital.overall.rating)
## [1] 3
# [1] 3
# The current rating of the provider is 3 in the hospital general info provided by CMS.
as.numeric(provider_140010_gp_scores$newcluster_id)
## [1] 5
# [1] 5
# The rating as per the kmeans model is 5, which is a +2 error
value = provider_140010_gp_scores$final_score * 100
value
## [1] 6.3
# Let us compare the provider's final score with the overall scores.
summary(all_scores$final_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.261000 -0.025000 0.000000 -0.002328 0.024000 0.250000
final_plot <- ggplot(all_scores, aes(x = newcluster_id, y = final_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Final Scores with the Provider Final Score") +
theme_light() +
geom_hline(aes(yintercept = value), color = "blue") +
geom_text(aes(0, value, label = value, vjust = -1, hjust = -1)) +
scale_y_continuous(breaks = 100 * seq(-0.2, 0.2, 0.05))
final_plot
# let us see how the provider score looks in all the group measures.
f1 <- provider_140010_gp_scores$final_score * 100
v1 <- provider_140010_gp_scores$radm_score * 100
v2 <- provider_140010_gp_scores$mort_score * 100
v3 <- provider_140010_gp_scores$safety_score * 100
v4 <- provider_140010_gp_scores$expe_score * 100
v5 <- provider_140010_gp_scores$medi_score * 100
v6 <- provider_140010_gp_scores$time_score * 100
v7 <- provider_140010_gp_scores$effe_score * 100
f <- final_plot + geom_hline(yintercept = f1, col = "black")
r <- readm_plot +
geom_hline(yintercept = v1, col = "maroon") +
geom_text(aes(0, v1, label = v1, vjust = -1, hjust = -1))
m <- mort_plot +
geom_hline(yintercept = v2, col = "maroon") +
geom_text(aes(0, v2, label = v2, vjust = 1, hjust = -1))
s <- safe_plot +
geom_hline(yintercept = v3, col = "maroon") +
geom_text(aes(0, v3, label = v3, vjust = -1, hjust = -1))
ex <- expe_plot +
geom_hline(yintercept = v4, col = "maroon") +
geom_text(aes(0, v4, label = v4, vjust = -1, hjust = -1))
md <- medi_plot +
geom_hline(yintercept = v5, col = "maroon") +
geom_text(aes(0, v5, label = v5, vjust = -1, hjust = -1))
t <- time_plot +
geom_hline(yintercept = v6, col = "maroon") +
geom_text(aes(0, v6, label = v6, vjust = -1, hjust = -1))
ef <- effe_plot +
geom_hline(yintercept = v7, col = "maroon") +
geom_text(aes(0, v7, label = v7, vjust = -1, hjust = -1))
# Let us plot all the above graphs with the provider's y intercept for each group's score.
grid_plot3 <- plot_grid(f, r, m, s, ex, md, t, ef,
labels = c('Final', 'Readmission', 'Mortality', 'Safety', 'Experience', 'Medical', 'Timeliness', 'Effectiveness'),
ncol = 2, nrow = 4)
grid_plot3
# On comparing the average scores of groups with the provider's score, we find that:
# We see that the final score of the provider is above final score of rating 4.
# 1. The final score is higher than the average score for rating=4 but lower than for rating=5
# 2. Readmissions score is between avg of ratings 3 and 4
# 3. Mortality score is better than average for rating=5
# 4. Safety score is between the avg score for rating = 2 and 1
# 5. Experience score is above the average of ratings 3 and below the average of 4
# 6. Medical score is very bad compared to all the average ratings scores.
# 7. Timeliness score is better than average for rating 5
# 8. Effectiveness score is better than average for rating for 4 and below the average of rating 5
# We can observe that the overall ratings accross Final, Readminssion, Mortality, Timeliness and effectiveness
# is very good compared.
# The areas of improvement are needed in Safety, Experience and Medical.
# Though medical has very less weightage, let us focus on drilling down Safety, Experience and Medical scores,
# which have 22% weightage.
# Now let us view how the median values of each group is looking with respect to the providers scores.
str(all_scores)
## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
all_median_scores <- round(summarise_all(all_scores[, -c(1, 10, 11)], .funs = (median), na.rm = T), 3)
all_median_scores
## radm_score mort_score safety_score expe_score medi_score time_score
## 1 0.002 0.004 0.007 -0.007 0.03 0.016
## effe_score final_score
## 1 0.014 0
all_median_scores <- cbind(id = "median_score", all_median_scores)
new_prov <- provider_140010_gp_scores[, -c(1, 10, 11)]
new_prov <- cbind(id = "provider_score", new_prov)
all_median_scores <- rbind(all_median_scores, new_prov)
all_median_scores
## id radm_score mort_score safety_score expe_score
## 1 median_score 0.002 0.004 0.007 -0.007
## 637 provider_score 0.019 0.278 -0.022 -0.002
## medi_score time_score effe_score final_score
## 1 0.030 0.016 0.014 0.000
## 637 -0.013 0.068 0.015 0.063
# id radm_score mort_score safety_score exp_score medi_score time_score effe_score final_score
# 1 median_score 0.002 0.004 0.007 -0.007 0.030 0.016 0.014 0.000
# 637 provider_score 0.019 0.278 -0.022 -0.002 -0.013 0.068 0.015 0.063
str(all_scores)
## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
str(all_scores)
## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
all_mean_scores <- round(summarise_all(all_scores[, -c(1, 10, 11)], .funs = (mean), na.rm = T), 3)
all_mean_scores
## radm_score mort_score safety_score expe_score medi_score time_score
## 1 0 0.004 0 -0.013 0.007 -0.008
## effe_score final_score
## 1 0.008 -0.002
all_mean_scores <- cbind(id = "mean_score", all_mean_scores)
new_prov <- provider_140010_gp_scores[, -c(1, 10, 11)]
new_prov <- cbind(id = "provider_score", new_prov)
all_mean_scores <- rbind(all_mean_scores, new_prov)
all_mean_scores
## id radm_score mort_score safety_score expe_score
## 1 mean_score 0.000 0.004 0.000 -0.013
## 637 provider_score 0.019 0.278 -0.022 -0.002
## medi_score time_score effe_score final_score
## 1 0.007 -0.008 0.008 -0.002
## 637 -0.013 0.068 0.015 0.063
# id radm_score mort_score safety_score expe_score medi_score time_score effe_score final_score
# mean_score 0.000 0.004 0.000 -0.013 0.007 -0.008 0.008 -0.002
# provider_score 0.019 0.278 -0.022 -0.002 -0.013 0.068 0.015 0.063
# The safety scores are lower than the overall average score, experience score is a bit okay with respect
# to the overall average, but the medical scores are very low.
# let us start the drill down in this order- Safety, Experience and Medical
# Let us plot the safety scores graph and identify the concern points
#———–#
# we will plot the provider score in the y intercept.
h1 <- provider_140010_gp_scores$safety_score * 100
safe_plot +
geom_hline(yintercept = h1, col = "maroon") +
geom_text(aes(0, h1, label = h1, vjust = -1, hjust = -1))
safety_scores <- safe_master[, -c(2:8)]
summary(safety_scores)
## Provider.ID COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score
## Min. : 10001 Min. :-4.2624 Min. :-7.5390 Min. :-5.7477
## 1st Qu.:140185 1st Qu.:-0.6403 1st Qu.:-0.4020 1st Qu.:-0.4872
## Median :260037 Median : 0.0745 Median : 0.1505 Median : 0.1293
## Mean :267984 Mean : 0.0007 Mean : 0.0016 Mean : 0.0051
## 3rd Qu.:390209 3rd Qu.: 0.6105 3rd Qu.: 0.6539 3rd Qu.: 0.7788
## Max. :670112 Max. : 2.5012 Max. : 1.0575 Max. : 1.0540
## NA's :2104 NA's :2443 NA's :1929
## HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
## Min. :-4.5045 Min. :-4.405 Min. :-5.8483 Min. :-4.7375
## 1st Qu.:-0.5577 1st Qu.:-0.547 1st Qu.:-0.4477 1st Qu.:-0.5783
## Median : 0.1646 Median : 0.201 Median : 0.1887 Median : 0.0363
## Mean : 0.0006 Mean : 0.000 Mean : 0.0011 Mean : 0.0010
## 3rd Qu.: 0.7073 3rd Qu.: 1.018 3rd Qu.: 0.6400 3rd Qu.: 0.6300
## Max. : 1.2061 Max. : 1.018 Max. : 1.1289 Max. : 1.5849
## NA's :2775 NA's :3962 NA's :2988 NA's :1546
## PSI_90_SAFETY_score
## Min. :-6.0575
## 1st Qu.:-0.4039
## Median : 0.0863
## Mean : 0.0009
## 3rd Qu.: 0.5764
## Max. : 2.3064
## NA's :1594
# Replace the NA's with median values
median_na <- function(x) {
x[which(is.na(x))] <- median(x, na.rm = T)
return(x)
}
safety_scores[, 2:ncol(safety_scores)] <- sapply(safety_scores[, -1], median_na)
summary(safety_scores)
## Provider.ID COMP_HIP_KNEE_score HAI_1_SIR_score
## Min. : 10001 Min. :-4.26238 Min. :-7.53897
## 1st Qu.:140185 1st Qu.:-0.10422 1st Qu.: 0.15046
## Median :260037 Median : 0.07446 Median : 0.15046
## Mean :267984 Mean : 0.03293 Mean : 0.07709
## 3rd Qu.:390209 3rd Qu.: 0.25314 3rd Qu.: 0.15046
## Max. :670112 Max. : 2.50121 Max. : 1.05750
## HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score
## Min. :-5.74767 Min. :-4.50448 Min. :-4.4049 Min. :-5.8483
## 1st Qu.:-0.05786 1st Qu.: 0.16460 1st Qu.: 0.2012 1st Qu.: 0.1887
## Median : 0.12929 Median : 0.16460 Median : 0.2012 Median : 0.1887
## Mean : 0.05481 Mean : 0.09507 Mean : 0.1655 Mean : 0.1174
## 3rd Qu.: 0.31644 3rd Qu.: 0.16460 3rd Qu.: 0.2012 3rd Qu.: 0.1887
## Max. : 1.05402 Max. : 1.20615 Max. : 1.0177 Max. : 1.1289
## HAI_6_SIR_score PSI_90_SAFETY_score
## Min. :-4.73747 Min. :-6.05751
## 1st Qu.:-0.27104 1st Qu.:-0.05792
## Median : 0.03625 Median : 0.08625
## Mean : 0.01234 Mean : 0.02916
## 3rd Qu.: 0.31830 3rd Qu.: 0.34575
## Max. : 1.58486 Max. : 2.30644
# The safety scores for the provider
provider_safe_scores <- round(safety_scores[which(safety_scores$Provider.ID == 140010),], 3)
provider_safe_scores
## Provider.ID COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score
## 1125 140010 -0.462 -0.112 -0.214
## HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
## 1125 -0.244 1.018 0.191 -0.291
## PSI_90_SAFETY_score
## 1125 -3.23
# Provider.ID COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score PSI_90_SAFETY_score
# 140010 -0.462 -0.112 -0.214 -0.244 1.018 0.191 -0.291 -3.23
# Let us calculate the average scores and compare them
average_safety_scores <- round(summarise_all(safety_scores[, -1], .funs = (mean), na.rm = T), 3)
average_safety_scores <- cbind(id = "mean_score", average_safety_scores)
average_safety_scores
## id COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score
## 1 mean_score 0.033 0.077 0.055
## HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
## 1 0.095 0.166 0.117 0.012
## PSI_90_SAFETY_score
## 1 0.029
# id COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score PSI_90_SAFETY_score
# mean_score 0.033 0.077 0.055 0.095 0.166 0.117 0.012 0.029
prov_s <- round(safety_scores[which(safety_scores$Provider.ID == 140010),], 3)
prov_s <- cbind(id = "prov_s_score", prov_s[, -1])
prov_s
## id COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score
## 1125 prov_s_score -0.462 -0.112 -0.214
## HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
## 1125 -0.244 1.018 0.191 -0.291
## PSI_90_SAFETY_score
## 1125 -3.23
# id Provider.ID COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score PSI_90_SAFETY_score
# prov_s_score 140010 -0.462 -0.112 -0.214 -0.244 1.018 0.191 -0.291 -3.23
average_safety_scores <- rbind(average_safety_scores, prov_s)
average_safety_scores
## id COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score
## 1 mean_score 0.033 0.077 0.055
## 1125 prov_s_score -0.462 -0.112 -0.214
## HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
## 1 0.095 0.166 0.117 0.012
## 1125 -0.244 1.018 0.191 -0.291
## PSI_90_SAFETY_score
## 1 0.029
## 1125 -3.230
# id COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score PSI_90_SAFETY_score
# mean_score 0.033 0.077 0.055 0.095 0.166 0.117 0.012 0.029
# prov_s_score -0.462 -0.112 -0.214 -0.244 1.018 0.191 -0.291 -3.230
# Except the HAI_4_SIR and HAI_5_SIR measures all the other measures are way below the average scores.
# Let us visualize how these low score measures are performing with respect to the ratings
# merging safety scores with all_scores
str(all_scores)
## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
safety_scores <- merge(safety_scores, all_scores, by = "Provider.ID")
str(safety_scores)
## 'data.frame': 2414 obs. of 19 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ COMP_HIP_KNEE_score: num -1.355 0.0745 -1.355 -1.1763 0.4318 ...
## $ HAI_1_SIR_score : num -2.3512 -1.023 0.3895 -0.0337 0.1505 ...
## $ HAI_2_SIR_score : num -2.088 0.05 -0.357 0.184 1.054 ...
## $ HAI_3_SIR_score : num -1.135 0.723 0.818 -0.582 0.165 ...
## $ HAI_4_SIR_score : num 1.018 0.201 0.201 1.018 0.201 ...
## $ HAI_5_SIR_score : num 0.647 -0.457 -0.312 0.118 0.189 ...
## $ HAI_6_SIR_score : num 0.0566 0.7984 0.5887 0.2256 1.148 ...
## $ PSI_90_SAFETY_score: num 1.2108 0.2304 -0.1156 -0.5769 -0.0579 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id : Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
# Measure: COMP_HIP_KNEE_score
summary(safety_scores$COMP_HIP_KNEE_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.26238 -0.46158 0.07446 -0.01261 0.61050 2.50121
s1 <- prov_s$COMP_HIP_KNEE_score * 100
CHK_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = COMP_HIP_KNEE_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("COMP_HIP_KNEE_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
safe_chk_p <- CHK_plot +
geom_hline(yintercept = s1, col = "black") +
geom_text(aes(0, s1, label = s1, vjust = -1, hjust = -1))
safe_chk_p
# The provider's score for COMP_HIP_KNEE_score is way below the average scores for all the ratings.
# This is a cause of concern, the provider has to improve on this score to improve their ratings.
# Measure: HAI_1_SIR_score
summary(safety_scores$HAI_1_SIR_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -7.53897 -0.25261 0.15046 0.05063 0.55402 1.05750
s2 <- prov_s$HAI_1_SIR_score * 100
HAI1_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = HAI_1_SIR_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("HAI_1_SIR_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-7, 1, 0.5))
safe_hai1_p <- HAI1_plot +
geom_hline(yintercept = s2, col = "black") +
geom_text(aes(0, s2, label = s2, vjust = -1, hjust = -1))
safe_hai1_p
# HAI_1_SIR score of the provider is lower than the average scores for ratings 3, 4, 5 and
# just below the average score for rating 2.
# Measure: HAI_2_SIR_score
summary(safety_scores$HAI_2_SIR_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -5.74767 -0.47399 0.10837 -0.02428 0.61588 1.05402
s3 <- prov_s$HAI_2_SIR_score * 100
HAI2_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = HAI_2_SIR_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("HAI_2_SIR_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-5, 1, 0.5))
safe_hai2_p <- HAI2_plot +
geom_hline(yintercept = s3, col = "black") +
geom_text(aes(0, s3, label = s3, vjust = -1, hjust = -1))
safe_hai2_p
# HAI_2_SIR score of the provider is lower than the average scores for ratings 3, 4, 5 and
# just below the average scores for ratings 1 & 2.
# Measure: HAI_3_SIR_score
summary(safety_scores$HAI_3_SIR_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.50448 -0.29764 0.16460 0.03225 0.51700 1.20615
s4 <- prov_s$HAI_3_SIR_score * 100
HAI3_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = HAI_3_SIR_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("HAI_3_SIR_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-5, 1, 0.5))
safe_hai3_p <- HAI3_plot +
geom_hline(yintercept = s4, col = "black") +
geom_text(aes(0, s4, label = s4, vjust = -1, hjust = -1))
safe_hai3_p
# HAI_3_SIR score of the provider is way below the average scores for all the ratings.
# This is a cause of concern, the provider has to improve on this score to improve their ratings.
# Measure: HAI_6_SIR_score
summary(safety_scores$HAI_6_SIR_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.73747 -0.61863 -0.08511 -0.13336 0.41540 1.58486
s5 <- prov_s$HAI_6_SIR_score * 100
HAI6_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = HAI_6_SIR_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("HAI_6_SIR_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-5, 1, 0.5))
safe_hai6_p <- HAI6_plot +
geom_hline(yintercept = s5, col = "black") +
geom_text(aes(0, s5, label = s5, vjust = -1, hjust = -1))
safe_hai6_p
# HAI_6_SIR score of the provider is below the average scores for all the ratings.
# This is a cause of concern, the provider has to improve on this score to improve their ratings.
# Measure: PSI_90_SAFETY_score
summary(safety_scores$PSI_90_SAFETY_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -6.0575 -0.5193 0.1151 -0.0289 0.6918 2.3064
s6 <- prov_s$PSI_90_SAFETY_score * 100
PSI90_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = PSI_90_SAFETY_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("PSI_90_SAFETY_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-5, 2, 0.75))
safe_psi90_p <- PSI90_plot +
geom_hline(yintercept = s6, col = "black") +
geom_text(aes(0, s6, label = s6, vjust = -1, hjust = -1))
safe_psi90_p
# PSI_90_SAFETY score of the provider is way below the average scores for all the ratings.
# The provider is doing poorly in this measure.
# This is a cause of concern, the provider has to improve on this score to improve their ratings.
# Let us plot all the safety graphs together.
grid_plot4 <- plot_grid(safe_chk_p, safe_hai1_p, safe_hai2_p, safe_hai3_p, safe_hai6_p, safe_psi90_p,
labels = c('COMP_HIP_KNEE_score', 'HAI_1_SIR_score', 'HAI_2_SIR_score', 'HAI_3_SIR_score', 'HAI_6_SIR_score', 'PSI_90_SAFETY_score'),
ncol = 2, nrow = 3)
grid_plot4
#————–#
# we will plot the provider score in the y intercept.
h2 <- provider_140010_gp_scores$expe_score * 100
expe_plot +
geom_hline(yintercept = h2, col = "maroon") +
geom_text(aes(0, h2, label = h2, vjust = -1, hjust = -1))
expe_scores <- expe_master[, -c(2:8)]
summary(expe_scores)
## Provider.ID H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## Min. : 10001 Min. :-3.1829 Min. :-4.8617
## 1st Qu.:140185 1st Qu.:-0.5938 1st Qu.:-0.5235
## Median :260037 Median :-0.0759 Median : 0.2652
## Mean :267984 Mean : 0.0003 Mean : 0.0007
## 3rd Qu.:390209 3rd Qu.: 0.7008 3rd Qu.: 0.6596
## Max. :670112 Max. : 2.7721 Max. : 2.6314
## NA's :1310 NA's :1310
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## Min. :-4.4601 Min. :-4.3050 Min. :-4.8584
## 1st Qu.:-0.7772 1st Qu.:-0.5175 1st Qu.:-0.6064
## Median : 0.0412 Median :-0.0626 Median : 0.1667
## Mean : 0.0010 Mean : 0.0001 Mean : 0.0000
## 3rd Qu.: 0.4504 3rd Qu.: 0.6198 3rd Qu.: 0.5533
## Max. : 2.9056 Max. : 2.6670 Max. : 3.0972
## NA's :1310 NA's :1310 NA's :1310
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## Min. :-3.9139 Min. :-4.5537 Min. :-4.0139
## 1st Qu.:-0.6485 1st Qu.:-0.5344 1st Qu.:-0.5316
## Median : 0.0513 Median : 0.0276 Median : 0.1649
## Mean : 0.0001 Mean : 0.0005 Mean : 0.0005
## 3rd Qu.: 0.5178 3rd Qu.: 0.5897 3rd Qu.: 0.5131
## Max. : 3.2190 Max. : 2.2758 Max. : 3.6472
## NA's :1310 NA's :1310 NA's :1310
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## Min. :-4.5255 Min. :-3.4634 Min. :-4.5030
## 1st Qu.:-0.5304 1st Qu.:-0.5967 1st Qu.:-0.6938
## Median : 0.0842 Median :-0.0098 Median : 0.2213
## Mean : 0.0013 Mean : 0.0004 Mean : 0.0015
## 3rd Qu.: 0.6988 3rd Qu.: 0.7729 3rd Qu.: 0.6788
## Max. : 2.7213 Max. : 2.7295 Max. : 2.5089
## NA's :1310 NA's :1310 NA's :1310
# Replace the NA's with median values
expe_scores[, 2:ncol(expe_scores)] <- sapply(expe_scores[, -1], median_na)
summary(expe_scores)
## Provider.ID H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## Min. : 10001 Min. :-3.18290 Min. :-4.8617
## 1st Qu.:140185 1st Qu.:-0.33486 1st Qu.:-0.1292
## Median :260037 Median :-0.07595 Median : 0.2652
## Mean :267984 Mean :-0.02043 Mean : 0.0726
## 3rd Qu.:390209 3rd Qu.: 0.44188 3rd Qu.: 0.2652
## Max. :670112 Max. : 2.77210 Max. : 2.6314
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## Min. :-4.46010 Min. :-4.30501 Min. :-4.85845
## 1st Qu.:-0.36803 1st Qu.:-0.29003 1st Qu.:-0.21983
## Median : 0.04118 Median :-0.06257 Median : 0.16672
## Mean : 0.01196 Mean :-0.01693 Mean : 0.04532
## 3rd Qu.: 0.45038 3rd Qu.: 0.39235 3rd Qu.: 0.16672
## Max. : 2.90563 Max. : 2.66695 Max. : 3.09723
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## Min. :-3.91389 Min. :-4.553676 Min. :-4.01386
## 1st Qu.:-0.41523 1st Qu.:-0.253380 1st Qu.:-0.18335
## Median : 0.05126 Median : 0.027637 Median : 0.16488
## Mean : 0.01403 Mean : 0.007893 Mean : 0.04519
## 3rd Qu.: 0.28451 3rd Qu.: 0.308654 3rd Qu.: 0.51311
## Max. : 3.21899 Max. : 2.275770 Max. : 3.64717
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## Min. :-4.52553 Min. :-3.463444 Min. :-4.50301
## 1st Qu.:-0.22313 1st Qu.:-0.401084 1st Qu.:-0.23626
## Median : 0.08419 Median :-0.009761 Median : 0.22126
## Mean : 0.02384 Mean :-0.002380 Mean : 0.06124
## 3rd Qu.: 0.39150 3rd Qu.: 0.381562 3rd Qu.: 0.45003
## Max. : 2.72131 Max. : 2.729500 Max. : 2.50890
# The experience scores for the provider
provider_expe_scores <- round(expe_scores[which(expe_scores$Provider.ID == 140010),], 3)
provider_expe_scores
## Provider.ID H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## 1125 140010 0.183 -0.129
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 1125 -0.368 -0.29 -0.22
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## 1125 -0.648 -0.815 -0.183
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 1125 0.084 -0.205 0.45
# Provider.ID H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
# 140010 0.183 -0.129 -0.368 -0.29 -0.22 -0.648 -0.815
# H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
# -0.183 0.084 -0.205 0.45
# Let us calculate the average scores and compare them
average_expe_scores <- round(summarise_all(expe_scores[, -1], .funs = (mean), na.rm = T), 3)
average_expe_scores <- cbind(id = "mean_score", average_expe_scores)
average_expe_scores
## id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## 1 mean_score -0.02 0.073
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 1 0.012 -0.017 0.045
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## 1 0.014 0.008 0.045
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 1 0.024 -0.002 0.061
# id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
# mean_score -0.02 0.073 0.012 -0.017 0.045 0.014 0.008
# H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
# 0.045 0.024 -0.002 0.061
prov_e <- round(expe_scores[which(expe_scores$Provider.ID == 140010),], 3)
prov_e <- cbind(id = "prov_e_score", prov_e[, -1])
prov_e
## id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## 1125 prov_e_score 0.183 -0.129
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 1125 -0.368 -0.29 -0.22
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## 1125 -0.648 -0.815 -0.183
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 1125 0.084 -0.205 0.45
# id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
# prov_e_score 0.183 -0.129 -0.368 -0.29 -0.22 -0.648 -0.815
# H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
# -0.183 0.084 -0.205 0.45
average_expe_scores <- rbind(average_expe_scores, prov_e)
average_expe_scores
## id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## 1 mean_score -0.020 0.073
## 1125 prov_e_score 0.183 -0.129
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 1 0.012 -0.017 0.045
## 1125 -0.368 -0.290 -0.220
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## 1 0.014 0.008 0.045
## 1125 -0.648 -0.815 -0.183
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 1 0.024 -0.002 0.061
## 1125 0.084 -0.205 0.450
# id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
# mean_score -0.020 0.073 0.012 -0.017 0.045 0.014 0.008
# prov_e_score 0.183 -0.129 -0.368 -0.290 -0.220 -0.648 -0.815
# H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
# 0.045 0.024 -0.002 0.061
# -0.183 0.084 -0.205 0.450
# The experience measures are very important.
# We notice that the provider's scores are very low compared to the average scores of H_COMP_1_LINEAR_SCORE, H_COMP_2_LINEAR_SCORE, H_COMP_3_LINEAR_SCORE,
# H_COMP_4_LINEAR_SCORE, H_COMP_5_LINEAR_SCORE, H_COMP_6_LINEAR_SCORE, H_COMP_7_LINEAR_SCORE, H_QUIET_LINEAR_SCORE.
# The scores for H_CLEAN_LINEAR_SCORE and H_RECMND_LINEAR_SCORE are very good compared to the average scores.
# Let us visualize how these low score measures are performing with respect to the ratings
# merging expe scores with all_scores
str(all_scores)
## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
expe_scores <- merge(expe_scores, all_scores, by = "Provider.ID")
str(expe_scores)
## 'data.frame': 2414 obs. of 22 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ H_CLEAN_LINEAR_SCORE : num -0.853 -1.112 -1.112 -1.629 -0.335 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.524 -0.129 -0.129 -0.524 0.265 ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0412 0.8596 0.8596 0.0412 0.8596 ...
## $ H_COMP_3_LINEAR_SCORE : num -1.1999 -0.29 -0.5175 -0.745 -0.0626 ...
## $ H_COMP_4_LINEAR_SCORE : num -0.606 0.167 -0.22 -0.606 0.167 ...
## $ H_COMP_5_LINEAR_SCORE : num -0.415 0.285 -0.182 -1.115 -0.415 ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0276 0.3087 -1.0964 -0.2534 0.3087 ...
## $ H_COMP_7_LINEAR_SCORE : num 0.165 -0.183 -0.532 -0.183 -0.88 ...
## $ H_HSP_RATING_LINEAR_SCORE: num 0.0842 0.3915 -1.1451 -0.2231 0.0842 ...
## $ H_QUIET_LINEAR_SCORE : num 0.969 0.577 0.577 0.186 0.577 ...
## $ H_RECMND_LINEAR_SCORE : num 0.45 0.221 -0.923 -0.236 -0.694 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id : Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
# Measure: H_COMP_1_LINEAR_SCORE
summary(expe_scores$H_COMP_1_LINEAR_SCORE)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.8617 -0.5235 -0.1292 -0.1290 0.2652 2.2371
e1 <- prov_e$H_COMP_1_LINEAR_SCORE * 100
HC1L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_1_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_1_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc1l_p <- HC1L_plot +
geom_hline(yintercept = e1, col = "black") +
geom_text(aes(0, e1, label = e1, vjust = -1, hjust = -1))
expe_hc1l_p
# The H_COMP_1_LINEAR_SCORE of the provider (-0.129) is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
# Measure: H_COMP_2_LINEAR_SCORE
summary(expe_scores$H_COMP_2_LINEAR_SCORE)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.46010 -0.77724 0.04118 -0.18292 0.45038 2.49642
e2 <- prov_e$H_COMP_2_LINEAR_SCORE * 100
HC2L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_2_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_2_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc2l_p <- HC2L_plot +
geom_hline(yintercept = e2, col = "black") +
geom_text(aes(0, e2, label = e2, vjust = -1, hjust = -1))
expe_hc2l_p
# The H_COMP_2_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
# Measure: H_COMP_3_LINEAR_SCORE
summary(expe_scores$H_COMP_3_LINEAR_SCORE)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.30501 -0.74495 -0.06257 -0.20950 0.39235 2.43949
e3 <- prov_e$H_COMP_3_LINEAR_SCORE * 100
HC3L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_3_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_3_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc3l_p <- HC3L_plot +
geom_hline(yintercept = e3, col = "black") +
geom_text(aes(0, e3, label = e3, vjust = -1, hjust = -1))
expe_hc3l_p
# The H_COMP_3_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
# Measure: H_COMP_4_LINEAR_SCORE
summary(expe_scores$H_COMP_4_LINEAR_SCORE)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.4719 -0.6064 -0.2198 -0.1337 0.5533 2.4860
e4 <- prov_e$H_COMP_4_LINEAR_SCORE * 100
HC4L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_4_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_4_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc4l_p <- HC4L_plot +
geom_hline(yintercept = e4, col = "black") +
geom_text(aes(0, e4, label = e4, vjust = -1, hjust = -1))
expe_hc4l_p
# Measure: H_COMP_5_LINEAR_SCORE
summary(expe_scores$H_COMP_5_LINEAR_SCORE)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.9139 -0.6485 -0.1820 -0.1808 0.2845 3.0834
e5 <- prov_e$H_COMP_5_LINEAR_SCORE * 100
HC5L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_5_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_5_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc5l_p <- HC5L_plot +
geom_hline(yintercept = e5, col = "black") +
geom_text(aes(0, e5, label = e5, vjust = -1, hjust = -1))
expe_hc5l_p
# The H_COMP_5_LINEAR_SCORE of the provider is above the ratings 1 and is equal to the avaerage score of rating 2 and lower than the average score of ratings 3, 4 & 5.
# Measure: H_COMP_6_LINEAR_SCORE
summary(expe_scores$H_COMP_6_LINEAR_SCORE)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.55368 -0.53440 0.02764 -0.05276 0.58967 2.27577
e6 <- prov_e$H_COMP_6_LINEAR_SCORE * 100
HC6L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_6_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_6_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc6l_p <- HC6L_plot +
geom_hline(yintercept = e6, col = "black") +
geom_text(aes(0, e6, label = e6, vjust = -1, hjust = -1))
expe_hc6l_p
# The H_COMP_6_LINEAR_SCORE of the provider is above the ratings 1 and lower than the average score of ratings 2, 3, 4 & 5.
# Measure: H_COMP_7_LINEAR_SCORE
summary(expe_scores$H_COMP_7_LINEAR_SCORE)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.0139 -0.5316 -0.1833 -0.1236 0.5131 3.6472
e7 <- prov_e$H_COMP_7_LINEAR_SCORE * 100
HC7L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_7_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_7_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc7l_p <- HC7L_plot +
geom_hline(yintercept = e7, col = "black") +
geom_text(aes(0, e7, label = e7, vjust = -1, hjust = -1))
expe_hc7l_p
# The H_COMP_7_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
# Measure: H_QUIET_LINEAR_SCORE
summary(expe_scores$H_QUIET_LINEAR_SCORE)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.463444 -0.792407 -0.009761 -0.187534 0.381562 1.946854
e8 <- prov_e$H_QUIET_LINEAR_SCORE * 100
HQL_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_QUIET_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_QUIET_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-3, 2, 0.5))
expe_hql_p <- HQL_plot +
geom_hline(yintercept = e8, col = "black") +
geom_text(aes(0, e8, label = e8, vjust = -1, hjust = -1))
expe_hql_p
# The H_QUIET_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
grid_plot5 <- plot_grid(expe_hc1l_p, expe_hc2l_p, expe_hc3l_p, expe_hc4l_p, expe_hc5l_p, expe_hc6l_p, expe_hc7l_p, expe_hql_p,
labels = c('H_COMP_1_LINEAR_SCORE', 'H_COMP_2_LINEAR_SCORE', 'H_COMP_3_LINEAR_SCORE', 'H_COMP_4_LINEAR_SCORE',
'H_COMP_5_LINEAR_SCORE', 'H_COMP_6_LINEAR_SCORE', 'H_COMP_7_LINEAR_SCORE', 'H_QUIET_LINEAR_SCORE'),
ncol = 3, nrow = 3)
grid_plot5
#———–#
# we will plot the provider score in the y intercept.
h3 <- provider_140010_gp_scores$medi_score * 100
medi_plot +
geom_hline(yintercept = h3, col = "maroon") +
geom_text(aes(0, h3, label = h3, vjust = -1, hjust = -1))
medi_scores <- medi_master[, -c(2:8)]
summary(medi_scores)
## Provider.ID OP_10_score OP_11_score OP_13_score
## Min. : 10001 Min. :-6.5644 Min. :-8.0024 Min. :-3.9422
## 1st Qu.:140185 1st Qu.:-0.1401 1st Qu.:-0.0394 1st Qu.:-0.5537
## Median :260037 Median : 0.3191 Median : 0.3886 Median : 0.0428
## Mean :267984 Mean : 0.0005 Mean : 0.0011 Mean : 0.0010
## 3rd Qu.:390209 3rd Qu.: 0.5634 3rd Qu.: 0.5256 3rd Qu.: 0.6394
## Max. :670112 Max. : 0.8761 Max. : 0.5427 Max. : 2.3296
## NA's :1189 NA's :1469 NA's :2585
## OP_14_score OP_8_score
## Min. :-5.4208 Min. :-3.141
## 1st Qu.:-0.6490 1st Qu.:-0.652
## Median : 0.1540 Median : 0.098
## Mean : 0.0005 Mean : 0.000
## 3rd Qu.: 0.6893 3rd Qu.: 0.660
## Max. : 1.4922 Max. : 2.880
## NA's :2514 NA's :3294
medi_scores[, 2:ncol(medi_scores)] <- sapply(medi_scores[, -1], median_na)
summary(medi_scores)
## Provider.ID OP_10_score OP_11_score OP_13_score
## Min. : 10001 Min. :-6.56436 Min. :-8.0024 Min. :-3.94217
## 1st Qu.:140185 1st Qu.: 0.06509 1st Qu.: 0.2003 1st Qu.: 0.04281
## Median :260037 Median : 0.31913 Median : 0.3886 Median : 0.04281
## Mean :267984 Mean : 0.07911 Mean : 0.1193 Mean : 0.02344
## 3rd Qu.:390209 3rd Qu.: 0.48524 3rd Qu.: 0.4743 3rd Qu.: 0.04281
## Max. :670112 Max. : 0.87606 Max. : 0.5427 Max. : 2.32955
## OP_14_score OP_8_score
## Min. :-5.42084 Min. :-3.14132
## 1st Qu.: 0.15397 1st Qu.: 0.09767
## Median : 0.15397 Median : 0.09767
## Mean : 0.08057 Mean : 0.06667
## 3rd Qu.: 0.15397 3rd Qu.: 0.09767
## Max. : 1.49221 Max. : 2.87987
# The medical scores for the provider
provider_medi_scores <- round(medi_scores[which(medi_scores$Provider.ID == 140010),], 3)
provider_medi_scores
## Provider.ID OP_10_score OP_11_score OP_13_score OP_14_score
## 1125 140010 0.261 0.2 -0.454 0.315
## OP_8_score
## 1125 0.285
# Provider.ID OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
# 140010 0.261 0.2 -0.454 0.315 0.285
# Let us calculate the average scores and compare them
average_medi_scores <- round(summarise_all(medi_scores[, -1], .funs = (mean), na.rm = T), 3)
average_medi_scores <- cbind(id = "mean_score", average_medi_scores)
average_medi_scores
## id OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
## 1 mean_score 0.079 0.119 0.023 0.081 0.067
# id OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
# mean_score 0.079 0.119 0.023 0.081 0.067
prov_m <- round(medi_scores[which(medi_scores$Provider.ID == 140010),], 3)
prov_m <- cbind(id = "prov_m_score", prov_m[, -1])
prov_m
## id OP_10_score OP_11_score OP_13_score OP_14_score
## 1125 prov_m_score 0.261 0.2 -0.454 0.315
## OP_8_score
## 1125 0.285
# id OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
# prov_m_score 0.261 0.2 -0.454 0.315 0.285
average_medi_scores <- rbind(average_medi_scores, prov_m)
average_medi_scores
## id OP_10_score OP_11_score OP_13_score OP_14_score
## 1 mean_score 0.079 0.119 0.023 0.081
## 1125 prov_m_score 0.261 0.200 -0.454 0.315
## OP_8_score
## 1 0.067
## 1125 0.285
# id OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
# mean_score 0.079 0.119 0.023 0.081 0.067
# prov_m_score 0.261 0.200 -0.454 0.315 0.285
# The Medical group have only 4% weightage.
# We see that the provider's scores are very high compared to average scores of all the measures, except OP_13_score.
# OP_13_score has very low score compared to the average score.
# We will check how the scores for this column are.
# Let us visualize how these low score measures are performing with respect to the ratings
# merging medi scores with all_scores
str(all_scores)
## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
medi_scores <- merge(medi_scores, all_scores, by = "Provider.ID")
str(medi_scores)
## 'data.frame': 2414 obs. of 16 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ OP_10_score : num 0.251 -0.423 -0.277 0.642 0.28 ...
## $ OP_11_score : num 0.389 -1.204 -0.245 -1.238 -1.306 ...
## $ OP_13_score : num -1.2 -0.3052 2.3296 0.0428 0.7885 ...
## $ OP_14_score : num 0.207 -0.649 -0.97 -0.328 0.154 ...
## $ OP_8_score : num 0.2996 -0.3783 -0.7822 0.5737 0.0977 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
# Measure: OP_13_score
summary(medi_scores$OP_13_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.94217 -0.40460 0.04281 -0.01064 0.44050 2.32955
m1 <- prov_m$OP_13_score * 100
OP13_plot <- ggplot(medi_scores, aes(x = factor(as.character(newcluster_id)), y = OP_13_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("OP_13_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
medi_op13_p <- OP13_plot +
geom_hline(yintercept = m1, col = "black") +
geom_text(aes(0, m1, label = m1, vjust = -1, hjust = -1))
medi_op13_p
Code ENDS